System, method, and apparatus for providing optimized network resources

ABSTRACT

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and/or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is related to and claims priority from the following U.S. patents and patent applications. This application is a continuation of U.S. patent application Ser. No. 18/085,791, filed Dec. 21, 2022, which is a continuation of U.S. patent application Ser. No. 18/085,733, filed Dec. 21, 2022, which is a continuation-in-part of U.S. patent application Ser. No. 17/901,035, filed Sep. 1, 2022, which claims priority to and the benefit of U.S. Provisional Patent Application No. 63/370,184, filed Aug. 2, 2022. Each of the above listed applications is incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to spectrum analysis and management for electromagnetic signals, and more particularly for providing optimized network resources.

2. Description of the Prior Art

It is generally known in the prior art to provide wireless communications spectrum management for detecting devices and for managing the space. Spectrum management includes the process of regulating the use of radio frequencies to promote efficient use and gain net social benefit. A problem faced in effective spectrum management is the various numbers of devices emanating wireless signal propagations at different frequencies and across different technological standards. Coupled with the different regulations relating to spectrum usage around the globe effective spectrum management becomes difficult to obtain and at best can only be reached over a long period of time.

Another problem facing effective spectrum management is the growing need from spectrum despite the finite amount of spectrum available. Wireless technologies and applications or services that require spectrum have exponentially grown in recent years. Consequently, available spectrum has become a valuable resource that must be efficiently utilized. Therefore, systems and methods are needed to effectively manage and optimize the available spectrum that is being used.

Prior art patent documents include the following:

U.S. Pat. No. 11,395,149 for System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization by inventor Montalvo, filed Oct. 30, 2020 and issued Jul. 19, 2022, is directed to systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

U.S. Patent Publication No. 2018/0352441 for Devices, methods, and systems with dynamic spectrum sharing by inventors Zheng, et al., filed Jun. 4, 2018 and published Dec. 6, 2018, is directed to devices, methods, and systems with dynamic spectrum sharing. A wireless communication device includes a software-defined radio, a spectrum sensing sub-system, a memory, and an electronic processor. The software-defined radio is configured to generate an input signal, and wirelessly communicate with one or more radio nodes using a traffic data channel and a broadcast control channel. The spectrum sensing sub-system is configured to sense local spectrum information from the input signal. The electronic processor is communicatively connected to the memory and the spectrum sensing sub-system and is configured to receive the local spectrum information from the spectrum sensing sub-system, receive spectrum information from the one or more radio nodes, and allocate resources for the traffic data channel based on the local spectrum information and the spectrum information that is received from the one or more radio nodes.

U.S. Patent Publication No. 2018/0295607 for Method and apparatus for adaptive bandwidth usage in a wireless communication network by inventors Lindoff, et al., filed Oct. 10, 2017 and published Oct. 11, 2018, is directed to reconfiguration of a receiver bandwidth of the wireless device is initiated to match the second scheduling bandwidth, wherein the second scheduling bandwidth is larger than a first scheduling bandwidth currently associated with the wireless device, and wherein the first and second scheduling bandwidths respectively define the bandwidth used for scheduling transmissions to the wireless device.

U.S. Pat. No. 9,538,528 for Efficient co-existence method for dynamic spectrum sharing by inventors Wagner, et al., filed Oct. 6, 2011 and issued Jan. 3, 2017, is directed to an apparatus that defines a set of resources out of a first number of orthogonal radio resources and controls a transmitting means to simultaneously transmit a respective first radio signal for each resource on all resources of the set. A respective estimated interference is estimated on each of the resources of the set when the respective first radio signals are transmitted simultaneously. A first resource of the set is selected if the estimated interference on the first resource exceeds a first predefined level and, in the set, the first resource is replaced by a second resource of the first number of resources not having been part of the set. Each of the controlling and the estimating, the selecting, and the replacing is performed in order, respectively, for a predefined time.

U.S. Pat. No. 8,972,311 for Intelligent spectrum allocation based on user behavior patterns by inventors Srikanteswara, et al., filed Jun. 26, 2012 and issued Mar. 3, 2015, is directed to a platform to facilitate transferring spectrum rights is provided that includes a database to ascertain information regarding available spectrum for use in wireless communications. A request for spectrum use from an entity needing spectrum may be matched with available spectrum. This matching comprises determining a pattern in user requests overtime to optimize spectrum allocation. The Cloud Spectrum Services (CSS) process allows entities to access spectrum they would otherwise not have; it allows the end user to complete their download during congested periods while maintaining high service quality; and it allows the holder of rental spectrum to receive compensation for an otherwise idle asset.

U.S. Pat. No. 10,536,210 for Interference suppressing method and device in dynamic frequency spectrum access system by inventors Zhao, et al., filed Apr. 14, 2016 and issued Jan. 14, 2020, is directed to an interference suppressing method and device in a dynamic frequency spectrum access (DSA) system. The system includes: a frequency spectrum management device, a primary system including a plurality of primary devices, and a secondary system including a plurality of secondary devices. The method includes: transmitting position information of each of the secondary devices to the frequency spectrum management device; determining, by the frequency spectrum management device, a weight factor for a specific secondary device according to the received position formation; and performing a second-stage precoding, and in the second-stage precoding, adjusting, by using the weight factor, an estimated power of the specific secondary device leaking to the other secondary device.

U.S. Pat. No. 10,582,401 for Large scale radio frequency signal information processing and analysis system by inventors Mengwasser, et al., filed Apr. 15, 2019 and issued Mar. 3, 2020, is directed to a large-scale radio frequency signal information processing and analysis system that provides advanced signal analysis for telecommunication applications, including band capacity and geographical density determinations and detection, classification, identification, and geolocation of signals across a wide range of frequencies and across broad geographical areas. The system may utilize a range of novel algorithms for bin-wise processing, Rayleigh distribution analysis, telecommunication signal classification, receiver anomaly detection, transmitter density estimation, transmitter detection and location, geolocation analysis, telecommunication activity estimation, telecommunication utilization estimation, frequency utilization estimation, and data interpolation.

U.S. Pat. No. 10,070,444 for Coordinated spectrum allocation and de-allocation to minimize spectrum fragmentation in a cognitive radio network by inventors Markwart, et al., filed Dec. 2, 2011 and issued Sep. 4, 2018, is directed to An apparatus and a method by which a fragmentation probability is determined which indicates a probability of fragmentation of frequency resources in at least one network section for at least one network operating entity. Moreover, an apparatus and a method by which frequency resources in at least one network section are allocated and/or de-allocated, priorities of frequency resources are defined for at least one network operating entity individually, and allocating and/or de-allocating of the frequency resources for the at least one network operating entity is performed based on the priorities. For allocating and/or de-allocating of the frequency resources, also the fragmentation probability may be taken into account.

U.S. Patent Publication No. 2020/0007249 for Wireless signal monitoring and analysis, and related methods, systems, and devices by inventors Derr, et al., filed Sep. 12, 2019 and published Jan. 2, 2020, is directed to wireless signal classifiers and systems that incorporate the same may include an energy-based detector configured to analyze an entire set of measurements and generate a first single classification result, a cyclostationary-based detector configured to analyze less than the entire set of measurements and generate a second signal classification result; and a classification merger configured to merge the first signal classification result and the second signal classification result. Ensemble wireless signal classification and systems and devices the incorporate the same are disclosed. Some ensemble wireless signal classification may include energy-based classification processes and machine learning-based classification processes. Incremental machine learning techniques may be incorporated to add new machine learning-based classifiers to a system or update existing machine learning-based classifiers.

U.S. Patent Publication No. 2018/0324595 for Spectral sensing and allocation using deep machine learning by inventor Shima, filed May 7, 2018 and published Nov. 8, 2018, is directed to methods and systems for identifying occupied areas of a radio frequency (RF) spectrum, identifying areas within that RF spectrum that are unusable for further transmissions, and identifying areas within that RF spectrum that are occupied but that may nonetheless be available for additional RF transmissions are provided. Implementation of the method then systems can include the use of multiple deep neural networks (DNNs), such as convolutional neural networks (CNN's), that are provided with inputs in the form of RF spectrograms. Embodiments of the present disclosure can be applied to cognitive radios or other configurable communication devices, including but not limited to multiple inputs multiple output (MIMO) devices and 5G communication system devices.

U.S. Patent Publication No. 2017/0041802 for Spectrum resource management device and method by inventors Sun, et al., filed May 27, 2015 and published Feb. 9, 2017, is directed to a spectrum resource management device: determines available spectrum resources of a target communication system, so that aggregation interference caused by the target communication system and a communication system with a low right against a communication system with a high right in a management area does not exceed an interference threshold of the communication system with a high right; reduces available spectrum resources of the communication system with a low right, so that the interference caused by the communication system with a low right against the target communication system does not exceed an interference threshold of the target communication system; and updates the available spectrum resources of the target communication system according to the reduced available spectrum resources of the communication system with a low right, so that the aggregation interference does not exceed the interference threshold of the communication system with a high right.

U.S. Pat. No. 9,900,899 for Dynamic spectrum allocation method and dynamic spectrum allocation device by inventors Jiang, et al., filed Mar. 26, 2014 and issued Feb. 20, 2018, is directed to a dynamic spectrum allocation method and a dynamic spectrum allocation device. In the method, a centralized node performs spectrum allocation and transmits a spectrum allocation result to each communication node, so that the communication node operates at a corresponding spectrum resource in accordance with the spectrum allocation result and performs statistics of communication quality measurement information. The centralized node receives the communication quality measurement information reported by the communication node, and determines whether or not it is required to trigger the spectrum re-allocation for the communication node in accordance with the communication quality measurement information about the communication node. When it is required to trigger the spectrum re-allocation, the centralized node re-allocates the spectrum for the communication node.

U.S. Pat. No. 9,578,516 for Radio system and spectrum resource reconfiguration method thereof by inventors Liu, et al., filed Feb. 7, 2013 and issued, Feb. 21, 2017, is directed to a radio system and a spectrum resource reconfiguration method thereof. The method comprises: a Reconfigurable Base Station (RBS) divides subordinate nodes into groups according to attributes of the subordinate nodes, and sends a reconfiguration command to a subordinate node in a designated group, and the RBS and the subordinate node execute reconfiguration of spectrum resources according to the reconfiguration command; or, the RBS executes reconfiguration of spectrum resources according to the reconfiguration command; and a subordinate User Equipment (UE) accessing to a reconfigured RBS after interruption. The reconfiguration of spectrum resources of a cognitive radio system can be realized.

U.S. Pat. No. 9,408,210 for Method, device and system for dynamic frequency spectrum optimization by inventors Pikhletsky, et al., filed Feb. 25, 2014 and issued Aug. 2, 2016, is directed to a method, a device and a system for dynamic frequency spectrum optimization. The method includes: predicting a traffic distribution of terminal(s) in each cell of multiple cells; generating multiple frequency spectrum allocation schemes for the multiple cells according to the traffic distribution of the terminal(s) in each cell, wherein each frequency spectrum allocation scheme comprises frequency spectrum(s) allocated for each cell; selecting a frequency spectrum allocation scheme superior to a current frequency spectrum allocation scheme of the multiple cells from the multiple frequency spectrum allocation schemes according to at least two network performance indicators of a network in which the multiple cells are located; and allocating frequency spectrum(s) for the multiple cells using the selected frequency spectrum allocation scheme. This improves the utilization rate of the frequency spectrum and optimizes the multiple network performance indicators at the same time.

U.S. Pat. No. 9,246,576 for Apparatus and methods for dynamic spectrum allocation in satellite communications by inventors Yanai, et al., filed Mar. 5, 2012 and issued Jan. 26, 2016, is directed to a communication system including Satellite Communication apparatus providing communication services to at least a first set of communicants, the first set of communicants including a first plurality of communicants, wherein the communication services are provided to each of the communicants in accordance with a spectrum allocation corresponding thereto, thereby to define a first plurality of spectrum allocations apportioning a first predefined spectrum portion among the first set of communicants; and Dynamic Spectrum Allocations apparatus operative to dynamically modify at least one spectrum allocation corresponding to at least one of the first plurality of communicants without exceeding the spectrum portion.

U.S. Pat. No. 8,254,393 for Harnessing predictive models of durations of channel availability for enhanced opportunistic allocation of radio spectrum by inventor Horvitz, filed Jun. 29, 2007 and issued Aug. 28, 2012, is directed to a proactive adaptive radio methodology for the opportunistic allocation of radio spectrum is described. The methods can be used to allocate radio spectrum resources by employing machine learning to learn models, via accruing data over time, that have the ability to predict the context-sensitive durations of the availability of channels. The predictive models are combined with decision-theoretic cost-benefit analyses to minimize disruptions of service or quality that can be associated with reactive allocation policies. Rather than reacting to losses of channel, the proactive policies seek switches in advance of the loss of a channel. Beyond determining durations of availability for one or more frequency bands statistical machine learning also be employed to generate price predictions in order to facilitate a sale or rental of the available frequencies, and these predictions can be employed in the switching analyses The methods can be employed in non-cooperating distributed models of allocation, in centralized allocation approaches, and in hybrid spectrum allocation scenarios.

U.S. Pat. No. 6,990,087 for Dynamic wireless resource utilization by inventors Rao, et al., filed Apr. 22, 2003 and issued Jan. 24, 2006, is directed to a method for dynamic wireless resource utilization includes monitoring a wireless communication resource; generating wireless communication resource data; using the wireless communication resource data, predicting the occurrence of one or more holes in a future time period; generating hole prediction data; using the hole prediction data, synthesizing one or more wireless communication channels from the one or more predicted holes; generating channel synthesis data; receiving data reflecting feedback from a previous wireless communication attempt and data reflecting a network condition; according to the received data and the channel synthesis data, selecting a particular wireless communication channel from the one or more synthesized wireless communication channels; generating wireless communication channel selection data; using the wireless communication channel selection data, instructing a radio unit to communicate using the selected wireless communication channel; and instructing the radio unit to discontinue use of the selected wireless communication channel after the communication has been completed.

U.S. Pat. No. 10,477,342 for Systems and methods of using wireless location, context, and/or one or more communication networks for monitoring for, preempting, and/or mitigating pre-identified behavior by inventor Williams, filed Dec. 13, 2017 and issued Nov. 12, 2019, is directed to systems and methods of using location, context, and/or one or more communication networks for monitoring for, preempting, and/or mitigating pre-identified behavior. For example, exemplary embodiments disclosed herein may include involuntarily, automatically, and/or wirelessly monitoring/mitigating undesirable behavior (e.g., addiction related undesirable behavior, etc.) of a person (e.g., an addict, a parolee, a user of a system, etc.). In an exemplary embodiment, a system generally includes a plurality of devices and/or sensors configured to determine, through one or more communications networks, a location of a person and/or a context of the person at the location; predict and evaluate a risk of a pre-identified behavior by the person in relation to the location and/or the context; and facilitate one or more actions and/or activities to mitigate the risk of the pre-identified behavior, if any, and/or react to the pre-identified behavior, if any, by the person.

SUMMARY OF THE INVENTION

The present invention relates to spectrum analysis and management for electromagnetic signals, and more particularly for providing optimized network resources. Furthermore, the present invention relates to spectrum analysis and management for electromagnetic (e.g., radio frequency (RF)) signals, and for automatically identifying baseline data and changes in state for signals from a multiplicity of devices in a wireless communications spectrum, and for providing remote access to measured and analyzed data through a virtualized computing network. In an embodiment, signals and the parameters of the signals are identified and indications of available frequencies are presented to a user. In another embodiment, the protocols of signals are also identified. In a further embodiment, the modulation of signals, data types carried by the signals, and estimated signal origins are identified.

It is an object of this invention to prioritize and manage applications in the wireless communications spectrum, while also optimizing application performance.

In one embodiment, the present invention provides a system for dynamic spectrum utilization management in a private wireless network including a network slice or a subnetwork of the private wireless network, wherein the network slice or the subnetwork includes a radio access network (RAN) and a core network, at least one monitoring sensor operable to monitor an electromagnetic environment related to the private wireless network and to create measured data, a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing the measured data and a programmable rules and policy editor, wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment related to the private wireless network, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, wherein the wireless network resource optimization application is operable to use analyzed data from the at least one data analysis engine to create actionable data, and wherein network resources of the private wireless network are operable to be optimized using the actionable data.

In another embodiment, the present invention provides a system for dynamic spectrum utilization management in a private wireless network including a network slice or a subnetwork, wherein the network slice or the subnetwork includes a radio access network (RAN) and a core network, at least one radio frequency (RF) sensor operable to monitor an RF environment related to the private wireless network and to create RF data, wherein one or more of the at least one RF sensor is included in a base station, a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing the RF data, wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the RF environment, wherein the wireless network resource optimization application is operable to use analyzed data from the at least one data analysis engine to create actionable data, and wherein network resources of the private wireless network are operable to be optimized using the actionable data.

In yet another embodiment, the present invention provides a method for dynamic spectrum utilization management in a private wireless network including providing a network slice or a subnetwork of the private wireless network, wherein the network slice or the subnetwork includes a radio access network (RAN) and a core network, providing a wireless network resource optimization application, monitoring an electromagnetic environment related to the private wireless network using at least one monitoring sensor to create measured data, analyzing the measured data using the at least one data analysis engine to create analyzed data, wherein the at least one data analysis engine includes a detection engine and a learning engine, learning the electromagnetic environment using the learning engine, automatically detecting at least one signal of interest using the detection engine, the wireless network resource optimization application using analyzed data from the at least one data analysis engine to create actionable data, and optimizing network resources of the private wireless network using the actionable data.

These and other aspects of the present invention will become apparent to those skilled in the art after a reading of the following description of the preferred embodiment when considered with the drawings, as they support the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of a network.

FIG. 2 illustrates one embodiment of a RAN interface.

FIG. 3 illustrates one embodiment of a Multi-Access Edge Computing (MEC) layer.

FIG. 4 illustrates one embodiment of the at least one sensor unit operable to interface with the physical layer.

FIG. 5 illustrates possible functional splits between central and distributed units.

FIG. 6 illustrates another example of possible functional splits.

FIG. 7 illustrates 3GPP, Small Cell Forum (SCF), and O-RAN possible functional splits.

FIG. 8 illustrates an example of a traditional base station with a remote radio head (RRH).

FIG. 9 illustrates an example of a centralized RAN (C-RAN).

FIG. 10 illustrates one embodiment of an open, disaggregated RAN.

FIG. 11 illustrates one embodiment of several planes of operation described in the O-RAN Fronthaul specification.

FIG. 12 illustrates one embodiment of a fronthaul interface according to the O-RAN Fronthaul specification.

FIG. 13 illustrates one embodiment of the O-RU/SU part of the xRAN split.

FIG. 14 illustrates e-NB/g-NB architecture including an O-DU and a plurality of radio units (O-RUs).

FIG. 15 illustrates one embodiment of a Layer 1 split.

FIG. 16 is a table describing supported data flow for a single radio unit with a single component carrier.

FIG. 17 illustrates one example of dataflows between the O-DU and the O-RU excluding M-Plane dataflows.

FIG. 18 illustrates an example of the C-Plane and U-Plane packet exchange.

FIG. 19 is a table of default values used for the implementation of O-DU and O-RU/SU simulation with a mmWave scenario.

FIG. 20 is a table of default values used for the implementation of O-DU and O-RU/SU simulation with numerology 0.

FIG. 21 is a table of default values used for the implementation of O-DU and O-RU/SU simulation with numerology 1.

FIG. 22 illustrates one embodiment of an ORAN Fronthaul process.

FIG. 23 illustrates Configuration LLS-C1.

FIG. 24 illustrates Configuration LLS-C3.

FIG. 25 illustrates one embodiment of a native ethernet frame with VLAN.

FIG. 26 illustrates one embodiment of eCRPI header field definitions.

FIG. 27 illustrates bit allocations of ecpriRtcid/ecpriPcid.

FIG. 28 illustrates xRAN packet components for DL/UL IQ data messages.

FIG. 29 illustrates one embodiment of a common radio application header.

FIG. 30 illustrates one embodiment of a data section application header.

FIG. 31 illustrates one embodiment of an xRAN packet data payload.

FIG. 32 illustrates a table of section types and target scenarios.

FIG. 33 illustrates one embodiment of a radio application common header.

FIG. 34 illustrates a structure of a Section Type 0 message.

FIG. 35 illustrates a structure of a Section Type 1 message.

FIG. 36 illustrates an entire Section Type 1 message.

FIG. 37 illustrates a structure of a Section Type 3 message.

FIG. 38 illustrates an entire Section Type 3 message.

FIG. 39 illustrates a structure of a Section Type 5 message.

FIG. 40 illustrates a structure of a Section Type 6 message.

FIG. 41 illustrates the F1-C and F1-U protocol stacks.

FIG. 42 is a table of F1 interface functionalities.

FIG. 43 illustrates transfer of application data between the DU and CU-UP using the user plane of the F1-U.

FIG. 44 is a table of PDU Type 0 downlink user data.

FIG. 45 is a table of PDU Type 1 downlink delivery status.

FIG. 46 illustrates one embodiment of O-RAN defined nodes.

FIG. 47 illustrates one embodiment of an overall O-RAN architecture.

FIG. 47 also illustrates three control loops.

FIG. 48 illustrates disaggregated network functions in the O-RAN.

FIG. 49 illustrates an aggregated O-CU and O-DU, which form an E2 node.

FIG. 50 illustrates an aggregated near-RT MC, O-CU-CP, and O-CU-UP.

FIG. 51 illustrates an aggregated near-RT RIC, O-CU-CP, O-CU-UP, O-DU, and O-RU.

FIG. 52 illustrates one embodiment of near-RT RIC internal architecture.

FIG. 53 illustrates a centralized near-RT RIC.

FIG. 54 illustrates a fully distributed near-RT RIC

FIG. 55 illustrates one embodiment of E2 interface architecture.

FIG. 56 illustrates a 3GPP 5G with the SBA on the left and the MEC system on the right.

FIG. 57 illustrates one embodiment of an integrated MEC deployment.

FIG. 58A illustrates a MEC and the local UPF co-located with the base station.

FIG. 58B illustrates a MEC co-located with a transmission node and optionally with a local UPF.

FIG. 58C illustrates a MEC and the local UPF co-located with a network aggregation point.

FIG. 58D illustrates a MEC co-located with the core network function (e.g., in the same data center).

FIG. 59 illustrates one embodiment of application mobility in an integrated MEC deployment.

FIG. 60 illustrates one embodiment of the MEC system operable to provide add-on services to at least one MEC application.

FIG. 61 illustrates one embodiment of the MEC system including a system level and distributed host level.

FIG. 62 illustrates one embodiment of a third-party cloud for MEC in a 5G network environment.

FIG. 63 illustrates one embodiment of a MEC host with and without a break-out to cloud.

FIG. 64 illustrates one embodiment of traffic steering to an alternative MEC host.

FIG. 65 illustrates one embodiment of a Distributed Autonomous Slice Management and Orchestration (DASMO).

FIG. 66 illustrates one embodiment of proposed generalized MEC architecture.

FIG. 67 illustrates one embodiment of DASMO extended with MEC.

FIG. 68 illustrates one embodiment of the system including resource optimization.

FIG. 69 illustrates one example of an RF environment.

FIG. 70 is a schematic diagram of a system of the present invention.

FIG. 71A illustrates one embodiment of an interference template including interference metrics, adjacent channel measurements, and geolocation.

FIG. 71B illustrates one embodiment of an interference template including additional evidence of external interference.

FIG. 72 illustrates one example of a graphical user interface (GUI) for a channel average power.

FIG. 73 illustrates one example of a GUI for channel availability.

DETAILED DESCRIPTION

The present invention is generally directed to spectrum analysis and management for electromagnetic signals, and more particularly for providing optimized network resources.

In one embodiment, the present invention provides a system for dynamic spectrum utilization management in a private wireless network including a network slice or a subnetwork of the private wireless network, wherein the network slice or the subnetwork includes a radio access network (RAN) and a core network, at least one monitoring sensor operable to monitor an electromagnetic environment related to the private wireless network and to create measured data, a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing the measured data and a programmable rules and policy editor, wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment related to the private wireless network, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, wherein the wireless network resource optimization application is operable to use analyzed data from the at least one data analysis engine to create actionable data, and wherein network resources of the private wireless network are operable to be optimized using the actionable data. In one embodiment, the network resources are optimized by reconfiguring at least one parameter of the core network and/or a Multi-Access Edge Computing (MEC) layer associated with the network slice or the subnetwork. In one embodiment, the network resources are optimized by changing at least one physical layer parameter of one or more customer devices and/or applications. In one embodiment, the at least one monitoring sensor is included in a base station and/or at least one spectrum monitoring unit, wherein the at least one spectrum monitoring unit includes at least one processor and memory, at least one receiver, and the at least one monitoring sensor. In one embodiment, the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio. In one embodiment, one or more of the at least one rule and/or the at least one policy is defined by requirements of at least one customer associated with the private wireless network. In one embodiment, the wireless network resource optimization application is operable to activate an alarm. In one embodiment, the at least one data analysis engine further includes an identification engine, a classification engine, and/or a geolocation engine. In one embodiment, the wireless network resource optimization application generates at least one RAN command to change at least one RAN parameter. In one embodiment, the wireless network resource optimization application creates a vector ensemble class for the at least one signal of interest. In one embodiment, the vector ensemble class includes one or more of: a center frequency, a bandwidth, a power, a signal to noise ratio, a signal to total noise and interference ratio, a modulation type, a type of signal, a location of the at least one signal of interest, an angle of arrival relative to a radio unit, an antenna index, an arrival rate, a time of arrival of the at least one signal of interest, a priority latency, an interaction vector including other signals with which the at least one signal of interest interacts, customer actionable data or information, a lower frequency component of the at least one signal of interest, an upper frequency component of the at least one signal of interest, and a time duration of the at least one signal of interest. In one embodiment, the wireless network resource optimization application creates a customer goals index vector. In one embodiment, the wireless network resource optimization application utilizes a constraint vector. In one embodiment, the wireless network resource optimization application creates the actionable data in real time or in near-real time. In one embodiment, the wireless network resource optimization application is operable to create the actionable data for one or more customers of the private wireless network simultaneously using at least one vector. In one embodiment, the actionable data is used to generate at least one knowledge-based decision tree. In one embodiment, the private wireless network utilizes spectrum in a plurality of bands, wherein the plurality of bands includes a Citizens Broadband Radio Service (CBRS) band. In one embodiment, the spectrum in the CBRS band is in a General Authorized Access (GAA) portion. In one embodiment, the private wireless network is operable to use Spectrum Access System (SaS) allocation methodology. In one embodiment, the actionable data is related to at least one interference event. In one embodiment, the optimization of the network resources based on the actionable data from the at least one interference event includes use of spectrum in a Citizens Broadband Radio Service (CBRS) band.

In another embodiment, the present invention provides a system for dynamic spectrum utilization management in a private wireless network including a network slice or a subnetwork, wherein the network slice or the subnetwork includes a radio access network (RAN) and a core network, at least one radio frequency (RF) sensor operable to monitor an RF environment related to the private wireless network and to create RF data, wherein one or more of the at least one RF sensor is included in a base station, a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing the RF data, wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the RF environment, wherein the wireless network resource optimization application is operable to use analyzed data from the at least one data analysis engine to create actionable data, and wherein network resources of the private wireless network are operable to be optimized using the actionable data.

In yet another embodiment, the present invention provides a method for dynamic spectrum utilization management in a private wireless network including providing a network slice or a subnetwork of the private wireless network, wherein the network slice or the subnetwork includes a radio access network (RAN) and a core network, providing a wireless network resource optimization application, monitoring an electromagnetic environment related to the private wireless network using at least one monitoring sensor to create measured data, analyzing the measured data using the at least one data analysis engine to create analyzed data, wherein the at least one data analysis engine includes a detection engine and a learning engine, learning the electromagnetic environment using the learning engine, automatically detecting at least one signal of interest using the detection engine, the wireless network resource optimization application using analyzed data from the at least one data analysis engine to create actionable data, and optimizing network resources of the private wireless network using the actionable data.

In one embodiment, the method further includes optimizing the network resources by reconfiguring at least one parameter of the core network and/or a Multi-Access Edge Computing (MEC) layer associated with the network slice or the subnetwork. In one embodiment, the method further includes optimizing the network resources by changing at least one physical layer parameter of one or more customer devices and/or applications. In one embodiment, the method further includes the wireless network resource optimization application creating a vector ensemble class for the at least one signal of interest including detected signal information. In one embodiment, the method further includes the wireless network resource optimization application utilizing a constraint vector.

The system is operable to monitor the electromagnetic (e.g., RF) environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. In a preferred embodiment, the system extracts the environmental awareness of the electromagnetic environment by including customer goals. In another embodiment, the system uses the environmental awareness with the customer goals and/or user defined policies and rules to extract actionable information to help the customer optimize the customer goals. The system combines and correlates other information sources with the extracted actionable information to enhance customer knowledge through dynamic spectrum utilization and prediction models.

In another embodiment, the at least one monitoring sensor includes at least one spectrum monitoring unit. Examples of monitoring units include those disclosed in U.S. Pat. Nos. 10,122,479, 10,219,163, 10,231,206, 10,237,770, 10,244,504, 10,257,727, 10,257,728, 10,257,729, 10,271,233, 10,299,149, 10,498,951, and 10,529,241, and U.S. Publication Nos. 20190215201, 20190364533, and 20200066132, each of which is incorporated herein by reference in its entirety. In one embodiment, the at least one spectrum monitoring unit includes a housing, at least one processor and memory, at least one receiver, and at least one sensor constructed and configured for sensing and measuring the electromagnetic environment.

FIG. 1 illustrates one embodiment of a network. The network (e.g., 5G network) includes both physical infrastructure and virtualized infrastructure. The physical infrastructure includes storage, computing resources, and networking resources. The virtualized infrastructure includes at least one radio access network (RAN), a multi-access edge computing (MEC) layer, and a core network. The core is operable to provide cellular and data services. The core provides network functions (NFs) and NF services. The core also is operable to provide network function virtualization (NFV) and software defined networking (SDN). NFV is operable to replace network functions (e.g., firewalls, load balancers, routers) with virtualized instances running as software. This eliminates the need to invest in many expensive hardware elements and can also accelerate installation times, thereby providing revenue generating services to the customer faster. The virtualized infrastructure further includes management and orchestration (MANO). The MANO is operable to coordinate network resources and network services. The MANO includes, but is not limited to, a NFV orchestrator (NFVO), a virtual network function manager (VNFM), and a virtual infrastructure manager (VIM). The virtualized infrastructure preferably also includes Operation Support Systems/Business Support Systems (OSS/BSS).

The present invention is operable to provide physical layer management operable to be optimized by slice or customer. This includes optimization of network resources based on physical layer measurements and/or slice or customer goals. The present invention is operable to aggregate a plurality of RAN resources associated with a slice or a customer subnetwork and optimize the plurality of RAN resources based on the RF environment and the quality of service (QoS) required by at least one application.

The network includes at least one network slice. Each network slice is administered by a mobile virtual network operator (MVNO). At least one tenant occupies each network slice (e.g., two tenants are operable to share a network slice) offered by the MVNO. At least one user equipment (UE) is connected to the network via 3rd Generation Partnership Project (3GPP) protocols and/or non-3GPP protocols using at least one radio access technology (RAT). The at least one RAT includes, but is not limited to, BLUETOOTH, WI-FI, Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long-Term Evolution (LTE), 5G New Radio (NR), long range (LoRa), and/or Push-to-Talk (PTT). The UE includes, but is not limited to, a smartphone, a mobile device, a tablet, a computer, a router, an internet of things (IoT) transceiver, and/or any other user equipment transmitting and/or receiving wireless radiofrequency messages. In one embodiment, one or more of the at least one UE includes embedded sub-sensors and/or is operable to perform RF analysis.

The at least one RAT interfaces with a radio access network (RAN). The RAN is operable to provide network connectivity to the at least one UE using at least one radio frequency. The RAN is operable to communicate with a multi-access edge computing (MEC) layer. The MEC is operable to provide resource optimization per application. The MEC is operable to communicate with a core. The core is operable to provide aggregated traffic policies and/or quality of service (QoS) recommendations.

In one embodiment, at least one sensor unit (SU) is used with a network (e.g., 5G network) to provide RF awareness in spectrum bands as shown in FIG. 1 . In one embodiment, the spectrum bands include spectrum bands of interest and/or adjacent bands to the spectrum bands of interest. The at least one SU is preferably operable to share information with the RAN, the MEC, and/or the core. In one embodiment, information from the at least one sensor unit is passed to the RAN, the MEC, and/or the core of each slice in the network as shown in FIG. 1 . By interacting with the MEC, applications that require optimization of physical layer resources (e.g., low latency, high reliability, high bandwidth, spectrum sharing) are operable to be optimized by properly assigning resources based on RF environmental conditions as well as network resource availability. Advantageously, properly assigning resources allows for the applications to work properly. The at least one sensor unit interfaces with the RAN to communicate resource assignment recommendations for implementation. The sensors also interface with the core in each slice through an Access and Mobility Management Function (AMF) in the core to help aggregate resources for applications and services requiring special physical layer parameters.

In one embodiment, the at least one sensor unit is collocated with the at least one RU in the RAN. Alternatively, the at least one sensor unit is within an area of coverage of the access point's RU (e.g., between 10 m to 1 km depending on the area of coverage of a single access or base station). In one embodiment, the at least one sensor unit connects to at least one DU in the RAN through a data format as described infra.

In one embodiment, one or more of the at least one sensor unit is operable to use existing antennas of the RUs in the RAN. In one embodiment, the one or more of the at least one sensor unit is included as an RU board within the RU structure in the RAN. In one embodiment, the one or more of the at least one sensor unit is assigned an identifier (e.g., RU identifier number). In one embodiment, the identifier is assigned using 3GPP and/or ORAN standards. In one embodiment, one or more of remaining sensor units (e.g., sensor units not operable to use existing antennas of the RUs in the RAN) are distributed into the DUs and/or CUs of the RAN. In one embodiment, antennas are deployed within the coverage range of each access point or base station (e.g., if one or more of the at least one sensor unit is not operable to use existing antennas of the RUs in the RAN) and resulting data is passed to the MEC through the core orchestration connection.

In one embodiment, the present invention includes a RAN interface operable to provide resource allocation recommendations, a MEC interface operable to provide resource optimization per application, and/or a core interface operable to provide aggregated traffic policies and/or quality of service (QoS) recommendations.

The system is operable to provide at least one network resources optimization recommendation from the MEC to the core. In one embodiment, during network setup and through the orchestration layer (e.g., using the MANO functions), a MEC function is configured to receive CU information from each RAN in the customer subnet or slice containing the RF awareness information and/or pertinent actionable data, which is routed to the network optimization application software. In one embodiment, the network optimization application software includes decision gates. In one embodiment, the network optimization application software is run in the MEC as a third-party function. In one embodiment, the core and/or the MEC are reconfigured through the MANO and/or the MEC orchestrator to act on data provided by the network optimization application software and/or allow the MEC application to generate RAN commands to change appropriate RAN parameters.

FIG. 2 illustrates one embodiment of a RAN interface. The at least one sensor unit is operable to act like an additional radio unit (RU), and thus is operable to interact with a base station. In one embodiment, the base station includes, but is not limited to, a Node B (NB), an eNode B (evolved NB), a gNB-CU (a next generation node base station central unit), a gNB-DU (a next generation node base station distributed unit), a base transceiver station (BTS), a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), and/or an access point (AP) (e.g., a wireless access point). In one embodiment, the at least one sensor unit is operable to interface with the base station through Non-Access Stratum (NAS) and Access Stratum (AS) functional layer protocols. The at least one sensor unit is further operable to interface with the core through the AMF to help affect connection management and resource aggregations per application.

As previously described, a Multi-Access Edge Computing (MEC) layer is included in the slice to support cloud computing at the slice portion of the network as shown in FIG. 3 . The MEC allows for applications and/or cloud computing at the edge of the network. The MEC level includes at least one MEC Host. The at least one MEC Host preferably includes a MEC Platform, MEC applications, and/or MEC Virtualization Infrastructure. In one embodiment, the MEC applications are hosted on a MEC application server. The MEC application server is preferably operable to support multi-tenancy. The MEC applications are provided by at least one equipment vendor, at least one customer, at least one service provider, and/or at least one third party. The at least one MEC Host is operable to provide a virtualization environment to run the MEC applications. The MEC Platform includes, but is not limited to, a MEC Service, a service registry, traffic rules control, and/or domain name service (DNS) handling. The MEC Platform is operable to run the MEC applications using the MEC Virtualization Infrastructure. The MEC Host is preferably operable to interface with at least one other MEC Host, which includes at least one other MEC Platform.

The MEC Host is in network communication with MEC Host Level Management. The MEC Host Level Management preferably includes a MEC Platform Manager and/or a Virtualization Infrastructure Manager (VIM). The MEC Platform Manager is operable to provide MEC Platform element management, MEC application rules and requests management, and/or MEC application life-cycle management. The MEC Host Level Management is operable to interface with system level resources including, but not limited to, a device application, a Customer Facing Service (CFS) portal, a user application life-cycle management (LCM) proxy, an operations support system, and/or a MEC orchestrator.

In one embodiment, the at least one sensor unit interfaces with an optimization application in the MEC Host as another application with its associate service registry and management as shown in FIG. 3 .

In one embodiment, the at least one sensor unit interfaces with the MEC through the core (e.g., the 5G slice's core) or the NAF function from the CU interface. In one embodiment, network resource optimization and/or customer decision gates (e.g., for mission critical applications) are conducted in the MEC. In one embodiment, the network resource optimization and/or the customer decision gates are provided as at least one MEC application in the MEC platform. In one embodiment, the at least one MEC application is configured in the MEC management layers through virtual infrastructure management at the orchestration and MANO levels. In one embodiment, the MEC is compatible with 3GPP release 16. The present invention is compatible with additional 3GPP protocols and releases.

In one embodiment, the at least one sensor unit is operable to interface with the physical layer (e.g., base stations, radio units, antennas, associated antenna forming logic) as shown in FIG. 4 . In one embodiment, the at least one sensor unit is operable to share at least one antenna with a base station. The at least one antenna preferably covers at least one band of interest and/or at least one adjacent band to the at least one band of interest to obtain situational awareness. Additionally or alternatively, the at least one sensor unit includes at least one antenna. In one embodiment, the at least one sensor unit captures data related to the RF environment. The at least one sensor unit is operable to analyze data captured by the at least one antenna from the base station and/or the at least one sensor unit, thereby creating analyzed data.

In one embodiment, information from the base station antennas is passed to the at least one sensor unit. In one embodiment, the RAN follows the O-RAN standards or the 3GPP new RAN standards, which provide for an interface to pass data (e.g., metadata) from each antenna to the RUs. In one embodiment, the data includes, but is not limited to, an antenna number, parameters associated with the antenna number, a beam number, parameters associated with the beam number, and/or I&Q data. In one embodiment, the system is operable to act as another RU or RU software embedded application in the RAN. In one embodiment, a destination RU is selected. In one embodiment, identification of the destination RU (e.g., using bits) is done during configuration and/or orchestration to add an RU independent of the existing RUs in the RAN.

Analyzed data is passed to a statistical inference and machine learning (ML) engine. The statistical inference and ML engine utilizes statistical learning techniques and/or control theory to learn the electromagnetic environment and make predictions about the electromagnetic environment. The statistical inference and ML engine is preferably programmed according to customer goals regarding a customer application. The statistical inference and data analysis engine preferably identifies relevant information required by the customer application to reach the customer goals. Advantageously, this decreases the amount of analyzed data that must be processed. The relevant information is used along with network information to identify physical layer resources (e.g., antenna, resource blocks, modulation parameters, bandwidth, spectrum sharing, spectrum aggregation) required for each application to perform properly. An optimization of resource assignment is conducted. In one embodiment, the optimization of resource assignment is conducted in the MEC as an application. Alternatively, the optimization of resource assignment is conducted in the at least one sensor unit.

In one embodiment, the statistical inference and ML engine interfaces with the MEC using at least one application. In one embodiment, the present invention follows the MEC application interphase protocols to communicate with the MEC MANO layer. Advantageously, performing optimization in the MEC as an application provides the lowest network transportation latency due to its proximity to the RAN. Further, performing optimization in the MEC as an application provides the ability to add computation and storage resources during configuration through the MANO and orchestration layer.

In one embodiment, the optimization of resource assignment is conducted in the at least one sensor unit. Advantageously, if the optimization is performed in the at least one sensor unit, no interface is needed with the RU, DU, and/or CU of the RAN. All data is passed to the MEC and core of all slices of the customer subnetwork. The main advantage is minimizing latency at the cost of power and computation limitations (e.g., optimizations are operable to be performed for a limited number of customer goals). In one embodiment, if multiple RU and DU are aggregated in a single CU, the system requires a plurality of sensor units.

As previously described, in one embodiment, the at least one sensor unit is operable to share at least one antenna with the base station. In one embodiment, no antenna forming logic is used, and antenna outputs are provided in the analog domain if a splitter is used for the at least one antenna. Alternatively, antenna forming logic is used, and antenna outputs are shared with the at least one sensor unit by passing information (e.g., beam identifier along with the antenna forming logic analog output or digital output). The at least one sensor unit is operable to derive associated I & Q data for RF environment analysis. Additionally or alternatively, the at least one sensor unit includes at least one antenna. For example, if the at least one antenna from the base station is not operable to be shared and/or does not include the at least one band of interest, then the at least one sensor unit includes at least one antenna and is operable to provide antenna forming logic. In one embodiment, the radio unit is operable to share I & Q data (e.g., RF, IF, or baseband), and the at least one sensor unit is operable to consume the I & Q data. The I & Q data is preferably time-stamped. Additionally, the at least one sensor unit is preferably provided with any information specifying receiver beam information to obtain information including, but not limited to, beam azimuth, elevation, and/or beamwidth.

As previously described, the system is operable to optimize resources (e.g., in a 5G network). Resource optimization is typically done in the core, where aggregated key performance indicators (KPIs) from different access points are used to evaluate conditions of the network and inform the network management layer. In 5G networks, the RAN functions of the slice's core still use these KPIs for each slice in the customer subnetwork. MAC and IP layers are optimized based on customer network performance preference in the MANO layers of each subnetwork. The present invention is operable to provide RF awareness and actionable data, thus performing physical layer optimization (e.g., in the MEC) and making suggestions to the core about physical layer parameters. In one embodiment, the RF awareness information is filtered by the customer goals to provide actionable data for each customer goal or mission critical application. Priorities between applications are preferably configured during network set up or pushed down to the MEC through the MANO orchestration layer. The MEC is operable to service multiple slices, and each slice's core is operable to be configured to share information through the orchestration and management layers.

Standards for 5G networks include, but are not limited to, (1) 3GPP TS 36.201, Version 17.0.0 (Mar. 31, 2022); (2) 3GPP TS 36.211, Version 17.2.0 (Jun. 23, 2022); (3) 3GPP TS 36.212, Version 17.1.0 (Apr. 1, 2022); (4) 3GPP TS 36.213, Version 17.2.0 (Jun. 23, 2022); (5) 3GPP TS 36.214, Version 17.0.0 (Mar. 31, 2022); (6) 3GPP TS 36.216, Version 17.0.0 (Mar. 31, 2022); (7) 3GPP TS 36.201, Version 16.0.0 (Jul. 14, 2020); (8) 3GPP TS 36.211, Version 16.7.0 (Sep. 28, 2021); (9) 3GPP TS 36.212, Version 16.7.0 (Jan. 5, 2022); (10) 3GPP TS 36.213, Version 16.8.0 (Jan. 5, 2022); (11) 3GPP TS 36.214, Version 16.2.0 (Mar. 31, 2021); (12) 3GPP TS 36.216, Version 16.0.0 (Jul. 14, 2020); (13) 3GPP TS 38.321, Version 17.1.0 (Jul. 17, 2022); (14) 3GPP TS 38.321, Version 16.8.0 (Jul. 17, 2022); (15) 3GPP TS 23.501, Version 17.5.0 (Jun. 15, 2022); (16) 3GPP TS 23.501, Version 16.13.0 (Jun. 15, 2022); (17) 3GPP TS 23.558, Version 17.4.0 (Jun. 13, 2022); (18) 3GPP TS 28.531, Version 17.4.0 (Jun. 16, 2022); and (19) 3GPP TS 28.531, Version 16.12.0 (Dec. 23, 2021), each of which is incorporated herein by reference in its entirety.

FIG. 8 illustrates an example of a traditional base station with a remote radio head (RRH). The base station or base band unit (BBU) and the RRH connected via a high bandwidth fiber connection (i.e., fronthaul). Generally, current LTE macro cell sites include equipment positioned at the bottom of a tower and an RRH positioned at the top of the tower.

FIG. 9 illustrates an example of a centralized RAN (C-RAN). A plurality of RRHs is connected to a BBU pool via a plurality of fronthaul connections. Generally, the BBU pool is located a few miles from the RRH.

A plurality of protocol standards including, but not limited to, Common Public Radio Interface (CPRI), Open Base Station Architecture Initiative (OBSAI), and Open Radio Equipment Interface (ETSI-ORI), govern transport of data on the fronthaul. CPRI is the most used standard for LTE. Advantageously, CPRI allows for customization, so RAN vendors included a significant amount of proprietary customization. However, this proprietary customization led to a significant amount of interoperability issues between radio equipment and baseband processing equipment from different vendors. To resolve the interoperability issues, an open, disaggregated RAN has been proposed as shown in FIG. 10 .

Additionally, transporting LTE data rates using CPRI transport required significant fiber bandwidth, which is expensive and created deployment problems. As such, many vendors have determined that CPRI is not sufficient for fronthaul requirements. 5G applications often require low latency, which makes fiber bandwidth a significant issue. In some embodiments, 5G is operable to deliver at least 10 times up to at least 100 times faster data rates than LTE. In one embodiment, a 5G base station is operable to deliver a data rate of at least 10 Gpbs. In comparison, an LTE base station is operable to deliver a data rate of less than 1 Gbps.

5G deployments utilize enhanced CPRI (eCPRI) or radio over ethernet (RoE) for transport on the fronthaul between DUs and RUs. eCPRI is a fronthaul protocol defined by the CPRI Forum. The eCPRI protocol is operable to deliver higher data rates via compression techniques. RoE is protocol defined by the IEEE 1914.3 working group. The RoE protocol defines encapsulation and mappings of radio protocols via ethernet frames. Option 7-2 supports both eCPRI and RoE transport options. The partition of features is operable to change the information payload between RAN components.

The O-RAN Fronthaul specification addresses the issues with interoperability between the radio equipment and baseband processing equipment. The O-RAN Fronthaul specification defines several planes of operation, including a control plane (C-Plane), user plane (U-Plane), a synchronization plane (S-Plane), and a management plane (M-Plane) as shown in FIG. 11 . The C-Plane is operable to utilize message to define scheduling and coordination required to transfer control signal data (e.g., beamforming for 5G). The U-Plane is operable to utilize messages to define transfer of user data. The S-Plane provides timing and synchronization between the DU and the RU. The M-Plane is operable to provide management of the radio unit via messages.

FIG. 12 illustrates one embodiment of a fronthaul interface according to the O-RAN Fronthaul specification. The O-RAN fronthaul provides communication between the O-RAN distributed unit (O-DU) and the O-RAN radio unit (O-RU). In one embodiment, the system includes a sensor unit (SU) including multiple hardware and software components.

In one embodiment, the SU is integrated into the DU. In one embodiment, the SU appears like a PHY algorithm and appends data to the WLS-CU interface message.

Hardware components include two networking ports. The two networking ports are operable to communication to the fronthaul network and the backhaul and/or midhaul network. The two networking ports are also operable to receive precision time protocol (PTP) synchronization. A system timer is operable to provide timing to the gNB application. Software components include a PTP software (e.g., a Linux PTP) to synchronize to the system timer to a global time (e.g., GPS timer), a PTP4L program operable to synchronize an oscillator on a network interface controller (NIC) to the PTP grandmaster (GM), a Phyc2sys program to synchronize the system timer to the oscillator on the NIC, a data plane development kit (DPDK) operable to provide an interface to an ethernet port, and/or an xRAN library to provide U-Plane and C-Plane functionality. PHY uses the xRAN library to access an interface to the O-RU. PHY uses a set of MAC/PHY APIs and a shared memory interface (WLS) to communicate with an L2 application. The L2 application is operable to use the backhaul and/or midhaul networking port to connect to the CU. The xRAN library and PHY are operable to communicate control (C-Plane) information, transmission time interval (TTI) information, symbol timing information, and/or IQ data.

FIG. 13 illustrates one embodiment of the O-RU/SU part of the xRAN split. In one embodiment, the at least one sensor unit is embedded in the ORAN block.

FIG. 14 illustrates e-NB/g-NB architecture including an O-DU and a plurality of radio units (O-RUs). In one embodiment, at least one SU is included in the e-NB/g-NB. In one embodiment, more than one RU and/or more than one SU is operable to be supported with the same implementation of the xRAN library.

In one embodiment, the SU is a stand alone unit (e.g., not incorporated into the RUs and/or DUs as computing software additions). In one embodiment, the stand-alone unit does not interface with the DUs and/or RUs in the ORAN. In one embodiment, the stand-alone unit interfaces with the MEC and/or the core.

As previously described, the O-RAN Fronthaul specification provides a plurality of splits of Layer 1 functionality between the O-DU and O-RU. FIG. 15 illustrates one embodiment of a Layer 1 split.

FIG. 16 is a table describing supported data flow for a single radio unit with a single component carrier. The U-Plane includes DL frequency domain IQ data, UL frequency domain IQ data, and PRACH frequency domain IQ data. The C-Plane includes scheduling commands. The S-plane includes timing and synchronization.

FIG. 17 illustrates one example of dataflows between the O-DU and the O-RU excluding M-Plane dataflows.

FIG. 18 illustrates an example of the C-Plane and U-Plane packet exchange. The example shown in FIG. 18 does not include a Quality of Service (QoS) request.

The O-RAN Fronthaul specification defines a latency model of the fronthaul interface and interactions between the O-DU and the O-RU. The xRAN library is operable to support a defined transport method. The O-RAN Fronthaul specification further defines network transport requirements, and O-DU transmit and receive windows are based on the network transport requirements. Delay characteristics of at least one RU and/or at least one SU are determined within the timing domain framework (e.g., GPS time).

FIG. 19 is a table of default values used for the implementation of O-DU and O-RU/SU simulation with a mmWave scenario. FIGS. 20-21 are tables of default values used for the implementation of O-DU and O-RU/SU simulation with numerology 0 and numerology 1 examples, respectively, as defined in the O-RAN standard for Sub6 scenarios. The configuration is operable to be adjusted via configuration files for sample application and reference PHY.

FIG. 22 illustrates one embodiment of an ORAN Fronthaul process. The O-DU includes an O-DU network interface card (NIC), and the O-RU includes a O-RU NIC. The O-DU and the O-RU are connected between the O-DU NIC and the O-RU NIC.

In one embodiment, the xRAN library provides support for transporting In-band and Quadrature (IQ) samples between the O-DU and O-RU/SU within the xRAN architecture based on functional split option 7-2. The xRAN library defines xRAN packet formats operable to be used to transport radio samples via the fronthaul according to the O-RAN Fronthaul specification. Additionally, the xRAN library is operable to provide functionality to generate at least one xRAN packet, append IQ samples in a payload of the at least one xRAN packet, and/or extract IQ samples from the at least one xRAN packet.

FIG. 23 illustrates Configuration LLS-C1. Configuration LLS-C1 provides network timing distribution from the O-DU and to the O-RU via a point-to-point topology and a synchronized ethernet (SyncE).

FIG. 24 illustrates Configuration LLS-C3. Configuration LLS-C3 provides network timing distribution from the primary reference time clock/telecom grand master clock (PRTC/T-GM). A network of switches includes at least one ethernet switch.

The O-RAN Fronthaul specification defines a list of mandatory functionality. However, the list of mandatory functionality for the O-DU includes features that are not currently supported. See, e.g., Transport Layer and ORAN Fronthaul Protocol Implementation (2019), O-RAN Project Revision 70d9d920, available at https://docs.o-ran-sc.org/projects/o-ran-sc-o-du-phy/en/latest/Transport-Layer-and-ORAN-Fronthaul-Protocol-Implementation_fh.html#section-type-3-structure (last accessed Mar. 20, 2022), which is incorporated herein by reference in its entirety. Table 5 of Transport Layer and ORAN Fronthaul Protocol Implementation (2019) lists ORAN Mandatory and Optional Feature Support. Table 6 of Transport Layer and ORAN Fronthaul Protocol Implementation (2019) lists levels of validation for xRAN functionality.

O-RAN Fronthaul data is operable to be transported over ethernet or IPv4/IPv6. However, current implementation of the xRAN library only supports ethernet with a virtual local area network (VLAN). FIG. 25 illustrates one embodiment of a native ethernet frame with VLAN.

The O-RAN Fronthaul specification defines the transport header based on the eCPRI specification. FIG. 26 illustrates one embodiment of eCRPI header field definitions. FIG. 27 illustrates bit allocations of ecpriRtcid/ecpriPcid.

In one embodiment, an xRAN packet includes compression. In one embodiment, an xRAN packet including compression includes a Compression Header after each Application Header. The O-RAN Fronthaul specification defines the Compression Header. Alternatively, the xRAN packet does not include compression. In one embodiment, the xRAN packet not including compression does not include the Compression Header.

FIG. 28 illustrates xRAN packet components for DL/UL IQ data messages. In one embodiment, octet M+9 through M+12 and octet M+17 through M+19 is specified as sensor data. In one embodiment, the sensor data includes a location and/or a directionality relative to the DU.

FIG. 29 illustrates one embodiment of a common radio application header. In one embodiment, the filterIndex=0, frameId=[0:99], subframeId=[0:9], slotId=[0:7], and symbolId=[0:13].

In one embodiment, the common radio application header is followed by an application header that is operable to be repeated for each data section within an eCPRI message. FIG. 30 illustrates one embodiment of a data section application header. In one embodiment, a single section is used for an ethernet packet where IQ samples startPrbu is equal to 0 and numPrbu is equal to the number of RBs used. The rb field is not normally used (value=0). In one embodiment, the present invention assigns the rb field a specific value of 1. The symInc field is not normally used (value=0). In one embodiment, the present invention assigns the symInc field a specific value of 1. Alternative configurations are compatible with the present invention. Advantageously, this format provides flexibility. For example, and not limitation, octet 9 allows up to 8 payload versions and 16 filter indexes to be set. In one embodiment, at least one payload version and/or at least one filter index is operable to pass UL sensor information (e.g., if the UE includes sensor capabilities). In another embodiment, the at least payload version and/or the at least one filter index is operable to pass UE parameter values that have been optimized in the DL.

An xRAN packet data payload includes a plurality of physical resource blocks (PRBs). In one embodiment, each PRB includes 12 IQ samples. In one embodiment, the xRAN packet data payload includes udCompParam to indicate that compression is enabled. FIG. 31 illustrates one embodiment of an xRAN packet data payload.

In one embodiment, a C-Plane message includes a first header layer and a second header layer. The first header layer includes a standard eCPRI header including a message type and the second header layer includes an application layer operable to provide control and synchronization.

FIG. 32 illustrates a table of section types and target scenarios.

FIG. 33 illustrates one embodiment of a radio application common header. The radio application common header is operable to be used for time reference.

FIG. 34 illustrates a structure of a Section Type 0 message. The Section Type 0 message is operable to indicate idle or guard periods from the O-DU to the open radio unit (O-RU).

FIG. 35 illustrates a structure of a Section Type 1 message. The Section Type 1 message is operable to The Section Type 1 message includes a user data compression header (udCompHdr) including IQ compression information.

FIG. 36 illustrates an entire Section Type 1 message. The entire Section Type 1 message includes the radio application common header, the radio application Section 1 header, and radio application Section 1 information.

FIG. 37 illustrates a structure of a Section Type 3 message. The Section Type 3 message includes the radio application common header, a timeOffset, a frameStructure, a cpLength, and the udCompHdr.

FIG. 38 illustrates an entire Section Type 3 message. The entire Section Type 3 message includes the radio application common header, the radio application Section 3 header, and radio application Section 3 information.

FIG. 39 illustrates a structure of a Section Type 5 message. The Section Type 5 message is operable to provide scheduling information for UEs.

FIG. 40 illustrates a structure of a Section Type 6 message. The Section Type 6 message is operable to provide UE channel information from the O-DU to the O-RU.

Communications between the DU and the CU are divided into control plane communications and user plane communications. The CU is the functional between control plane information and user plane information.

The F1 interface connects a gNodeB (gNB) CU to a gNB DU. The control plane of the F1 interface (F1-C) allows transfer of information between the CU and the DU, while the user plane of the F1 interface (F1-U) allows the transfer of user plane information (e.g., application data).

FIG. 41 illustrates the F1-C and F1-U protocol stacks. F1-C uses stream control transmission protocol (SCTP) over internet protocol (IP) and is defined in 3GPP TS 38.473, Version 16.8.0 (Dec. 23, 2021), which is incorporated herein by reference in its entirety. In contrast, F1-U uses GPRS tunneling protocol user plane (GTP-U) over user datagram protocol (UDP) over internet protocol (IP) for transport layer and is defined in 3GPP TS 38.425, Version 16.3.0 (Apr. 9, 2021), which is incorporated herein by reference in its entirety. GPRS Tunnelling Protocol (GTP) is a protocol to carry general packet radio service (GPRS) in 5G radio network.

The F1 interface is an open interface and general information about F1 is found in 3GPP TS 38.470, Version 16.5.0 (Jul. 1, 2021), which is incorporated herein by reference in its entirety. The F1 interface supports the exchange of signaling and data information between the endpoints. The F1 interface separates the control plane and the user plane. Additionally, the F1 interface separates the Radio Network Layer from the Transport Network Layer. The F1 interface allows for connection of a gNB-CU and a gNB-DU supplied by different manufacturers. The F1 interface enables exchange of user equipment (UE)-associated information and non-UE associated information.

FIG. 42 is a table of F1 interface functionalities. The F1 interface functionalities include, but are not limited to, interface management functions, system information (SI) management functions, UE context management functions, radio resource control (RRC) message transfer functions, paging functions, warning message transfer functions, remote interface management (RIM) functions, trace functions, load management functions, self-optimization support functions, positioning functions, and/or integrated access and backhaul (IAB) support functions. The interface management functions include, but are not limited to, reset procedures, error indication, F1 setup procedures, gNB-DU configuration updates, gNB-CU configuration updates, gNB-DU resource coordination, and/or gNB-status indication.

The reset procedures are operable to initialize or re-initialize F1AP UE contexts in event of a failure of the CU or the DU. In one embodiment, the failure occurs at the CU and a reset message is transmitted to the DU. The DU then releases all assigned F1 resources and related radio resources and responds with a reset acknowledge message. In one embodiment, the failure occurs at the DU and a reset message is transmitted to the CU. The CU releases all allocated F1 resources and responds with a reset acknowledge message.

The error indication procedure is operable to be initiated by the CU or the DU. In one embodiment, the error indication procedure is initiated by an error indication message. In one embodiment, the error indication message includes cause information and/or criticality diagnostic information.

The F1 setup procedures are operable to exchange application-level data for the CU and the DU to correctly interoperate. In one embodiment, the F1 setup procedure includes the DU initiating an F1 setup request to a CU, and the CU returning an F1 setup response message. In one embodiment, an SCTP connection is established between the CU and the DU before the F1 setup procedure is operable to be initiated. In one embodiment, the F1 setup request is operable to include an identification of the DU (e.g., name) and/or a set of cells served by the DU. In one embodiment, the F1 setup response is operable to provide a list of cells to be activated.

The gNB-DU configuration update is operable to update application-level configuration data for the gNB-DU and gNB-CU to interoperate correctly on the F1 interface. In one embodiment, the gNB-DU initiates the procedure by sending a gNB-DU configuration update message including updated configuration data (e.g., served cells to add, served cells to modify, served cells to delete, cells status). The CU returns a gNB-DU configuration update acknowledge message to the DU.

The gNB-CU configuration update is also operable to update application-level configuration data for the gNB-DU and gNB-CU to interoperate correctly on the F1 interface. In one embodiment, the gNB-CU initiates the procedure by sending a gNB-CU configuration update message including updated configuration data (e.g., cells to be activated, cells to be deactivated). The DU returns a gNB-CU configuration update acknowledge message to the CU.

The gNB-DU resource coordination procedure is operable to allow coordination of resource allocation between a gNB-CU and gNB-DU for spectrum sharing. In one embodiment, the gNB-CU initiates the procedure by sending a gNB-DU resource coordination request message to a gNB-DU to the DU. The DU returns a gNB-DU resource coordination response to the CU.

The gNB-DU status indication procedure is operable to inform the CU that the DU is overloaded to allow for overload reduction actions. In one embodiment, the gNB-DU initiates the procedure by sending a gNB-DU status indication message to the CU. The overload reduction actions are applied until an updated gNB-DU status indication message indicates that the DU is no longer overloaded.

The F1 interface includes a plurality of RRC message transfer procedures including, but not limited to, initial UL RRC message transfer, DL RRC message transfer, UL RRC message transfer, and/or RRC delivery report.

The initial UL RRC message transfer procedure is operable to send an initial RRC message from the gNB-DU to the gNB-CU. The DL RRC message transfer procedure is operable to send an RRC message from the gNB-CU to the gNB-DU. The UL RRC message transfer procedure is operable to send a UL RRC message transfer response from the gNB-DU to the gNB-CU. The RRC delivery report procedure is operable to send an RRC delivery report regarding successful delivery of messages from the gNB-CU to the gNB-DU.

The F1 interface includes a plurality of UE context management procedures including, but not limited to, UE context setup, UE context release request, UE context modification, UE inactivity notification, and/or notify.

The UE context setup procedure is operable to establish UE context. In one embodiment, the UE context is related to information including, but not limited to, SRB, DRB, BH RLC channel, and/or SL DRB configuration. The UE context setup procedure is operable to send a UE context setup request from the gNB-CU to the gNB-DU. The UE context setup procedure is operable to send a UE context setup response message from the gNB-DU to the gNB-CU.

The UE context modification procedure is operable to modify an established UE context (e.g., establish, modify, and/or release radio resources or sidelink resources). The UE context modification procedure includes a UE context modification request from the gNB-CU to the gNB-DU. The UE context modification procedure includes a UE context modification response.

The UE context modification required procedure is operable to modify the established UE context (e.g., modify and/or release radio bearer resources, sidelink radio bearer resources, and/or candidate cells). The UE context modification required procedure includes a UE context modification required message sent from the gNB-DU to the gNB-CU. The UE context modification required procedure includes a UE context modification confirm message sent from the gNB-CU to the gNB-DU.

The UE context release procedure is operable to enable the gNB-CU to order the release of an existing UE context. The UE context release procedure includes the gNB-CU sending a UE context release command to the gNB-DU. The UE context release procedure includes the gNB-DU sending a UE context release complete message to the gNB-CU.

The UE inactivity notification procedure is operable to indicate an inactivity status of a UE. In one embodiment, the gNB-DU sends a UE inactivity notification message to the gNB-CU.

The notify procedure is operable to allow the gNB-DU to inform the gNB-CU that a quality of service (QoS) is not operable to fulfilled or is operable to be fulfilled again. In one embodiment, the gNB-DU initiates the notify procedure by sending a notify message to the gNB-CU. In one embodiment, the notify message is operable to indicate alternative QoS parameters the gNB-DU is operable to fulfil.

In one embodiment, the F1 interface further includes a plurality of warning message transmission procedures including, but not limited to, a write-replace warning, a public warning system (PWS) cancel, a PWS restart indication, and/or a PWS failure indication.

The write-replace warning procedure is operable to start or overwrite the broadcasting of warning messages. In one embodiment, the gNB-CU initiates the write-replace warning procedure by sending a write-replace warning request message to the gNB-DU and the gNB-DU returns a write-replace warning response.

The PWS cancel procedure is operable to cancel broadcast of a warning message. In one embodiment, the gNB-CU initiates the PWS cancel procedure by sending a PWS cancel request to the gNB-DU and the gNB-DU returns a PWS cancel response.

The PWS restart indication procedure is operable to inform the gNB-CU that PWS information for at least one cell of the gNB-DU is available for reloading. In one embodiment, the gNB-DU initiates the PWS restart indication procedure by sending a PWS restart indication to the gNB-CU.

The PWS failure indication procedure is operable to inform the gNB-CU that ongoing PWS transmission of the gNB-DU has failed for at least one cell. In one embodiment, the gNB-DU sends a PWS failure indication message to the gNB-CU. The PWS failure indication message preferably includes identification of the at least one cell.

In one embodiment, the F1 interface includes a system information delivery procedure operable to command the gNB-DU to broadcast Other System Information (OSI). In one embodiment, the gNB-CU initiates the procedure by sending a system information delivery command to the DU.

In one embodiment, the F1 interface includes a paging procedure operable to providing paging information to allow a gNB-DU to page a UE. In one embodiment, the gNB-CU initiates the procedure by sending a paging message to the DU. The paging message includes an identity of the UE (e.g., RAN UE Paging Identity (I-RNTI), Core Network UE Paging Identity (S-TMSI)). In one embodiment, the paging message further includes a paging DRX to determine a final paging cycle for the UE, a paging priority, and/or a paging cell list.

FIG. 43 illustrates transfer of application data between the DU and CU-UP using the user plane of the F1-U. A Protocol Data Unit (PDU) Type 0 is transmitted from the CU to the DU, while a PDU Type 1 is transmitted from the DU to the CU. Application data is transferred via GTP-U tunnels, which are identified using a tunnel endpoint identifier (TED). Each data radio bearer (DRB) has a tunnel.

The user plane protocol uses services of the transport network layer to provide control mechanisms for transfer of downlink data. The control mechanisms include, but are not limited to, flow control, detection of lost data packets, and/or delivery status updates (e.g., reporting successful delivery).

The CU-UP uses a PDU Type 0 frame format to track sequence numbers for each downlink data packet. In one embodiment, the sequence numbers are used by the DU to detect lost data packets. In one embodiment, the PDU Type 0 frame format is used to indicate discard instructions. FIG. 44 is a table of PDU Type 0 downlink user data.

The DU uses a PDU Type 1 frame format to report lost data packets and provide flow control. The PDU Type 1 frame format is preferably operable to indicate a sequence number of the highest successfully delivered PDCP sequence number, the desired buffer size for the data radio bearer, and/or the desired data rate. In one embodiment, the desired data rate indicates an amount of data in bytes for the DU to receive within a specified time interval (e.g., 1 second). In one embodiment, the cause value is operable to indicate a radio link outage and/or a radio link resume. FIG. 45 is a table of PDU Type 1 downlink delivery status.

See, e.g., (1) 3GPP TS 38.470 5G NG-RAN: F1 general aspects and principles, Version 16.5.0 (dated Jul. 1, 2021); (2) 3GPP TS 38.473 5G NG-RAN: F1 Application Protocol (FLAP), Version 16.8.0 (dated Dec. 23, 2021); and 3GPP TS 38.425 5G NG-RAN: NR user plane protocol, Version 16.3.0 (dated Apr. 9, 2021), each of which is incorporated herein by reference in its entirety.

FIG. 46 illustrates one embodiment of O-RAN defined nodes. The O-CU-CP, O-CU-UP, O-DU, and O-RU are defined by 3GPP and adapted by O-RAN specifications. The O-Cloud is a cloud computing platform formed of physical infrastructure elements operable to host O-RAN nodes (e.g., near RT-RIC, O-DU) and operable to host virtual network functions used by components (e.g., RICs). The O-RAN radio unit (O-RU) is operable to process radio frequencies received by the PHY layer. The O-DU is a logical node hosting protocols including, but not limited to, radio resource control (RLC), medium access control (MAC), and/or the physical interface (PHY). The O-CU includes the O-CU-CP and the O-CU-UP. The O-CU-CP includes radio resource control (RRC) and control plane components of Packet Data Convergence Protocol (PDCP). The O-CU-UP includes Service Data Adaptation Protocol (SDAP) and user plane components of PDCP. The near-RT RIC is operable to enable near-RT control and/or optimization of O-RAN components and resources. In one embodiment, the near-RT RIC includes artificial intelligence and/or machine learning. The non-RT RIC is operable to enable non-RT control and/or optimization of O-RAN components and resources. In one embodiment, the non-RT MC is operable to provide policy-based management of the near-RT MC. An xApp is an application operable to run on the near-RT RIC. In one embodiment, the xApp is independent of the near-RT RIC. The Service Management and Orchestration framework (SMO) is operable to manage the near-RT RIC, non-RT MC, the O-Cloud, the O-CU, and/or the O-DU. Advantageously, different components are operable to be provided by different manufacturers.

FIG. 47 illustrates one embodiment of an overall O-RAN architecture. The A1 interface is defined between the non-RT MC in the SMO and the near-RT RIC. The A1 interface provides at least three types of services including, but not limited to, policy management services, enrichment information services, and machine learning (ML) model management services. The E2 interface is operable to communicate with the base station (e.g., O-RU, O-DU, O-CU). At least one message (e.g., monitor, suspend, override, control) is operable to control the base station and actions are operable to executed (e.g., from xApps, from near-RT RIC). The E2 interface is operable to collect data and receive feedback from the base station (e.g., O-RU, O-DU, O-CU). The O1 and Open-Fronthaul M-plane interfaces are a Fault, Configuration, Accounting, Performance, Security (FCAPS) interface. The O1 and Open-Fronthaul M-plane interfaces are operable to exchange information (e.g., configuration, registration, security, performance, monitoring) with components including, but not limited to, the O-CU-CP, O-CU-UP, the O-DU, the O-RU, and/or the near-RT RIC. The O2 interface is operable to manage communications between the SMO and the O-Cloud (e.g., management of platform resources and workload).

FIG. 47 also illustrates three control loops. The first control loop is a real-time control loop. In one embodiment, actions in the real-time control loop occur in less than 10 ms. The second control loop is a near-real-time control loop. In one embodiment, actions in the near-real-time control loop occur between 10 ms and 1 s (e.g., functions such as Traffic Steering, Mobility Management, Interface Management). The third control loop is a non-real-time control loop. In one embodiment, actions in the non-real-time control loop occur in greater than 1 s (e.g., orchestration and optimization functions, incorporation of ML models).

FIGS. 48-51 illustrate example embodiments for the implementation of O-RAN architecture. Descriptions of O-RAN architecture are included in “O-RAN Architecture-Description 6.0”, O-RAN.WG1.O-RAN-Architecture-Description-v06.00, O-RAN Alliance (March 2022), which is incorporated herein by reference in its entirety. In one embodiment, the E2 is a logical node terminating E2 interface. In one embodiment, the E2 node includes O-CU-CP, O-CU-UP, O-DU, or any combination specified by the O-RAN Alliance. The nodes are operable to be aggregated in a plurality of different ways including, but not limited to, (1) disaggregated network functions in the O-RAN, (2) aggregated O-CU-CP and O-CU-UP, (3) aggregated O-CU-CP, O-CU-UP, (4) aggregated near-RT RIC, O-CU-CP, and O-CU-UP, (5) aggregated O-CU-CP, O-CU-UP, O-DU, and O-RU, (6) aggregated O-DU and O-RU, or (7) aggregated near-RT RIC, O-CU-CP, O-CU-UP, O-DU, and O-RU.

FIG. 48 illustrates disaggregated network functions in the O-RAN. In the embodiment shown in FIG. 48 , the near-RT RIC has E2 connections to the O-CU-CP, the O-CU-UP, and the O-DU.

FIG. 49 illustrates an aggregated O-CU and O-DU, which form an E2 node. There is only one E2 connection from the near-RT RIC to the E2 node, and only one O1 connection from the SMO to the E2 node.

FIG. 50 illustrates an aggregated near-RT MC, O-CU-CP, and O-CU-UP. The E2 interfaces to control the O-CU-CP and the O-CU-UP are contained within the E2 node. The E2 node has an E2 interface with the O-DU.

FIG. 51 illustrates an aggregated near-RT RIC, O-CU-CP, O-CU-UP, O-DU, and O-RU. The E2 interface is fully internal. A single O1 connection is present between the SMO and the E2 node, as well as an A1 connection between the non-RT MC and the near-RT RIC.

The near-RT RIC hosts microservice-based applications known as xApps. xApps are operable to control a distributed collection of RAN components (e.g., eNB, gNB, CU, DU) via the E2 interface. The near-RT RIC also includes the A1 interface and the O1 interface to the non-RT MC for the management and optimization of the RAN. Advantageously, this allows the near-RT MC to utilize a plurality of RAN types (e.g., macro cells, small cells, massive MIMO) and/or a plurality of RAN data to manage and optimize the RAN. xApps are operable to utilize the E2 interface collect near-RT information. The near-RT RIC is operable to control E2 nodes via policies and data provided from the non-RT RIC via the A1 interface. The near-RT RIC includes machine learning (ML) models and is operable to provide loading balancing, handover control, interference management, and/or resource block management. The radio-network information base (R-NIB) is operable to determine a state of the network (e.g., in near RT) and transmit network state data to the RAN. In one embodiment, the network state data is used to train AI/ML models. In one embodiment, the AI/ML models are operable to facilitate radio resource management (RRM). In one embodiment, the non-RT RIC is operable to transmit trained models to the near-RT RIC via the A1 interface. The near-RT RIC is operable to execute the trained models to improve network conditions.

In one embodiment, the near-RT RIC operates in near-real time. In one embodiment, near-real time is greater than 10 ms and less than 1 s. In one embodiment, the near-RT RIC is operable to control and optimize RAN. In one embodiment, the near-RT RIC includes xApps. In one embodiment, the near-RT MC is operable to perform radio resource management (e.g., via xApps).

The near-RT RIC is described in “O-RAN Near-Real-time RAN Intelligent Controller Architecture & E2 General Aspects and Principles 2.01”, O-RAN.WG3.E2GAP-v02.01, O-RAN Alliance (March 2022), which is incorporated herein by reference in its entirety.

FIG. 52 illustrates one embodiment of near-RT RIC internal architecture. The near-RT RIC includes a plurality of functions hosted by xApps. The plurality of functions hosted by the xApps is operable to be executed at the near-RT RIC to provide services. Outcomes of the services are operable to be sent to the E2 nodes via the E2 interface. The database and the shared data layer are operable to allow reading and/or writing of information related to the RAN and/or UE. A conflict mitigation function is operable to resolve conflicts between at least two xApps. The xApp subscription management function is operable to merge subscriptions from a plurality of xApps. The security function is operable to provide security for the xApps. The management services function is operable to provide management (e.g., configuration, performance, lifecycle, fault) of xApps. The messaging infrastructure function is operable to provide messaging between internal functions of the near-RT RIC.

FIG. 53 illustrates a centralized near-RT RIC. In the centralized near-RT RIC, the entire gNB (e.g., via O-DU, O-CUs) and/or eNB are handled by the same near-RT RIC. Advantageously, this allows for the near-RT RIC to optimize operations and make overall decisions for an individual base station.

FIG. 54 illustrates a fully distributed near-RT RIC. In the fully distributed near-RT RIC, each E2 node is handled by a near-RT RIC entity including a plurality of near-RT RICs. Individual components of the E2 node (e.g., O-CU-CP, O-CU-UP, C-DU, and/or O-eNB) are optimized by one of the plurality of near-RT RICs.

Advantageously, the flexibility of implementation options allows for different components to be supplied and deployed by different companies. However, this requires more complexity in design of the E2 interface.

The E2 interface provides communication between the near-RT MC and E2 nodes. The near-RT RIC (e.g., via the xApps) are operable to control functions inside the E2 nodes. E2 interface architecture is described in “O-RAN Near-Real-time RAN Intelligent Controller Architecture & E2 General Aspects and Principles 2.01”, O-RAN.WG3.E2GAP-v02.01, O-RAN Alliance (March 2022), which is incorporated herein by reference in its entirety. The E2 interface is open and facilitates connectivity between the near-RT RIC and the E2 node supplied by different manufacturers. Further, the E2 interface facilitates exposure of selected E2 node data towards the near-RT RIC. The selected E2 node data includes, but is not limited to, configuration information (e.g., cell configuration, supported slices, public land mobile networks (PLMNs)), network measurements, and/or context information. The E2 interface further enables the near-RT RIC to control selected functions on the E2 node.

The E2 functions include, but are not limited to, near-RT RIC services and/or near-RT RIC support functions. The near-RT RIC services include, but are not limited to, REPORT, INSERT, CONTROL, and/or POLICY. The near-RT RIC support functions include, but are not limited to, interface management (e.g., E2 setup, E2 reset, E2 node configuration update, reporting of general error situations) and/or a near-RT RIC service update.

FIG. 55 illustrates one embodiment of E2 interface architecture. The near-RT MC includes a database and at least one xApp. The database is operable to store data from the at least one xApp and the E2 node. The database is also operable to provide data to the at least one xApp. The near-RT RIC further includes an E2 termination function that is operable to terminate the E2 interface. The E2 includes at least one RAN function controlled by the near-RT MC, an E2 agent, and other functions. In one embodiment, the E2 agent is operable to terminate the E2 interface. In one embodiment, the E2 agent is operable to forward and/or receive E2 messages. The at least one RAN function (e.g., Function (1), Function (N)) is controlled by the near-RT RIC and supports near-RT MC services. In one embodiment, the other functions are RAN functions that do not support near-RT RIC services.

One of the major challenges with implementing 5G networks is reducing latency. Although networking slicing and RAN modifications reduce delays relative to 3G and 4G networks, it is not enough to meet the requirements with 5G. As a result, services need to move to the edge of the network. Advantageously, this moves application services and content closer to users, providing low latency, optimized content distribution, localized data caching, and/or integration with internet of things (IoT) devices.

The integration of ETSI Multi-access Edge Computing (MEC) and 5G is viewed as a solution to the difficulty of meeting the latency requirements. One critical issue for the MEC is the management of physical resources (e.g., via at least one application). The MEC is operable to assist in traffic routing and policy control.

ETSI ISG MEC (Industry Specification Group for Multi-access Edge Computing) is responsible for developing the technical standards for edge computing. The ETSI ISG MEC has published a set of specifications for the MEC including, but limited to, the following: (1) ETSI GS MEC 003 V1.1.1, “Mobile Edge Computing (MEC); Framework and Reference Architecture” (2016 March); (2) ETSI GS MEC 010-1 V1.1.1, “Mobile Edge Computing (MEC); Mobile Edge Management; Part 1: System host and platform management” (2017 October); (3) ETSI GS MEC 010-2 V1.1.1, “Mobile Edge Computing (MEC); Mobile Edge Management; Part 2: Application lifecycle, rules and requirements management” (2017 July); (4) ETSI GS MEC 011 V1.1.1, “Mobile Edge Computing (MEC); Mobile Edge Platform Application Enablement” (2017 July); (5) ETSI GS MEC 012 V1.1.1, “Mobile Edge Computing (MEC); Radio Network Information” (2017 July); (6) ETSI GS MEC 013 V1.1.1, “Mobile Edge Computing (MEC); Location API” (2017 July); (7) ETSI GS MEC 014 V1.1.1, “Mobile Edge Computing (MEC); UE Identity API” (2018 February); (8) ETSI GS MEC 015 V1.1.1, “Mobile Edge Computing (MEC); Bandwidth Management API” (2017-10); and (9) ETSI GS MEC 016 V1.1.1, “Mobile Edge Computing (MEC); UE Application Interface” (2017 September), each of which is incorporated herein by reference in its entirety. The set of specifications include management and orchestration (MANO) of MEC applications, application enablement application programming interfaces (API), service APIs, and the user equipment (UE) API. The MANO and application enablement functions are operable to provide service environments in edge data center. The service APIs are operable to provide network information to application. Advantageously, the MEC provides contextual information and real-time status of the environment through the APIs. The UE API is operable to facilitation UE interaction with the MEC system.

Edge computing is one of the key technologies involved in 5G to support low latency, mission critical services, and/or IoT devices. The MEC is operable to be mapped onto application functions (AFs) that are operable to use services and/or information provided by network functions based on configuration policies. Further, the MEC is operable to be deployed in a plurality of configurations, which provides flexibility to the system. The MEC is operable to include a plurality of applications. The plurality of applications is operable to provide a plurality of services including, but not limited to, streaming (e.g., movies, television), gaming, IoT, and/or V2X communication.

The system architecture for 5G is specified by 3GPP in 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; System architecture for the 5G System (5GS); Stage 2 (Release 17) v17.4.0 (dated March 2022), which is incorporated herein by reference in its entirety. One of the significant changes to the 5G system is the ability to use either a point-to-point paradigm or a service based architecture (SBA) between core network functions.

The SBA includes a plurality of network functions that are operable to consume and/or produce at least one service. In one embodiment, the SBA includes a request-response model and/or a subscribe-notify model. ETSI ISG MEC defined an API framework operable to be used MEC applications. Both the API framework and the SBA are operable to provide efficient use of services using functions including, but not limited to, registration, service discovery, availability notifications, de-registration, authentication, and/or authorization. FIG. 56 illustrates a 3GPP 5G with the SBA on the left and the MEC system on the right.

The network functions and corresponding services produced by the network functions are operable to be registered in a network resource function (NRF). Services produced by MEC applications are operable to registered in a service registry of the MEC platform. A list of available services is operable to be discovered from the NRF. In one embodiment, a service is accessible only via the NEF. In one embodiment, the NEF is operable to authorize access requests external to the domain. An Authentication Server Function (AUSF) is operable to perform procedures related to authentication. A Network Slice Selection Function (NSSF) is operable to assist in selecting suitable network slice instances for user and allocating Access Management Functions. A MEC application is operable to belong to at least one network slice configured in the core network.

The Policy Control Function (PCF) is operable to handle policies and rules. An AF (e.g., a MEC platform) is operable to request services from the PCF to impact traffic steering rules. The PCF is operable to be accessed directly or via the NEF.

The unified data management (UDM) is operable to provide services related to users and subscriptions. The UDM is operable to manage data for authentication, user registration, and data network profiles.

The user plane function (UPF) is operable to connect data from the RAN to the Internet and/or route traffic for user devices to a base station. In one embodiment, UPFs are controlled via a network exposure function (NEF) to policy control function (PCF) to session management function (SMF) route. In one embodiment, the UPF is included in the MEC.

FIG. 57 illustrates one embodiment of an integrated MEC deployment. In the embodiment shown in FIG. 57 , the MEC orchestrator is included in the MEC system as a system level entity that is operable to interact as an application function with the network exposure function (NEF). In one embodiment, the MEC orchestrator is operable to interact as an application function directly with network functions. The MEC includes a host level with a MEC platform operable to interact as an application function with the network functions.

The NEF is located between the core network and external third-party applications. The NEF is operable to manage external network data and external applications. The N6 reference point is located between the UPF and a data network. In one embodiment, the MEC is deployed at the N6 reference point. In one embodiment, the MEC host includes a plurality of MEC applications, a message broker as a MEC platform service, and/or a MEC platform service operable to steer traffic. In one embodiment, an Access and Mobility Management Function (AMF) is operable to provide mobility related procedures. In one embodiment, the AMF is operable to terminate RAN control plane and/or Non-Access Stratum (NAS) procedures, protect the integrity of signaling, manage registrations, manage connections, manage reachability, interface with the lawful interception function (e.g., for access and mobility events), provide authentication and/or authorization for the access layer, and/or host Security Anchor Functionality (SEAF). The SEAF is operable to act as a “middleman” during authentication between a UE and a network. In one embodiment, the AMF is operable to provide communication and/or reachability services for at least one network function. In one embodiment, the AMF is operable to allow subscriptions to receive notifications regarding mobility events.

A Session Management Function (SMF) is operable to provide a plurality of functions including, but not limited to, session management internet protocol (IP) address allocation and management; Dynamic Host Configuration Protocol (DHCP) services; selection, reselection, and/or control of the UPF; configuring traffic rules for the UPF; interception for session management events; charging; and/or support for roaming. The SMF is operable to provide service operations to allow MEC to manage Protocol Data Unit (PDU) session, control policy sessions and traffic rules, and/or subscribe to notification on session management events.

MEC hosts are deployed in the edge or central data network. The UPF is operable to steer user plane traffic towards MEC applications in the data network. The MEC management system is operable to orchestrate operation of MEC hosts and applications. The MEC management system is further operable to dynamically deploy MEC applications.

FIGS. 58A-58D illustrate options for the physical location of MEC. FIG. 58A illustrates a MEC and the local UPF co-located with the base station. FIG. 58B illustrates a MEC co-located with a transmission node and optionally with a local UPF. FIG. 58C illustrates a MEC and the local UPF co-located with a network aggregation point. FIG. 58D illustrates MEC co-located with the core network function (e.g., in the same data center). Advantageously, the MEC is operable to be deployed in a plurality of ways in different locations (e.g., near the base station, near the central data network).

The MEC is operable to provide traffic steering to route traffic to targeted applications in a slice or a distributed cloud. The M2 reference point between the data plane and the MEC platform is operable to provide traffic steering instructions (e.g., for applications, networks, services, etc.) to the data plane.

The Mp2 reference point between the MEC platform and the data plane of the virtualization infrastructure is used to instruct the data plane on how to route traffic among applications, networks, services, etc. The User Plane Function (UPF) is operable to route traffic to applications and/or network functions. An Application Function (AF) is operable to influence selection and/or re-selection of a UPF. The AF is also operable to configure rules to provide traffic steering to a data network.

The AF is operable to map Functional Entities (FE) of the MEC system. Traffic is not routed to MEC application unless the MEC application is prepared to receive traffic and the MEC platform has configured the data plane to route traffic to the MEC application. A MEC FE is operable to interact with the PCF to request traffic steering. In one embodiment, the MEC FE transmits information identifying the traffic to be steered to the PCF. In one embodiment, the PCF is operable to analyze the request, form policies in response to the request (e.g., policies that apply to at least one PDU session), and/or provide routing rules to the SMF. In one embodiment, the SMF is operable to initiate configuration of the routing rules in a target UPF. If no target UPF exists, the SMF is operable to designate at least one UPF.

In one embodiment, data plane functionality of the MEC is governed by the UPF. In one embodiment, the UPF is influenced by the MEC via control plane interactions with core network functions. In one embodiment, the SMF is operable to configure the UPF with a plurality of options for traffic steering. In one embodiment, the SMF inserts an uplink classifier function (UL CL) in the data path (e.g., for IPv4, IPv6, IPv4v6, ethernet). In one embodiment, the UL CL includes traffic rules operable to forward uplink traffic towards at least one application and/or network function. In one embodiment, the UL CL is operable to merge traffic destined to at least one UE in the downlink direction.

In one embodiment, a PDU session uses IPv6 or IPv4v6. In one embodiment, the SMF is operable to use a multi-homing concept for traffic steering. In one embodiment, the SMF is operable to insert a branching point function in a target UPF and/or configure the target UPF to split uplink traffic to a location application instance and/or services in a central cloud based on source prefixes of the IP data packets.

Advantageously, the system is operable to enable traffic steering based on a plurality of parameters. In one embodiment, the system is operable to provide generic traffic rule setting. In another embodiment, the system is operable to provide specific traffic rule setting for at least one specific UE. In one embodiment, the plurality of parameters includes, but is not limited to, information to identify the traffic (e.g., DNN, S-NSSAI, AF-Service-Identifier), a reference identifier operable to provide preconfigured routing information, a list of DNASs, information about at least one UE, information regarding possibilities of relocating at least one application, a timeframe when a routing condition is valid, a geographic location when the routing condition is valid, a notification type for user plane management notification, and/or a transaction identifier for the AF. In one embodiment, the system is operable to allow MEC functional entities and/or a MEC orchestrator to monitor mobility events related to MEC application instances. In one embodiment, the MEC functional entities are operable to subscribe to user plane path management notifications from at least one SMF. In one embodiment, the user plane path management notifications are operable to initiate traffic configuration procedures and/or application relocation procedures.

The MEC system is operable to provide networking and computing at the edge of the network with low latency and high bandwidth. Providing services at the edge means that the system must be operable to provide UE mobility. For example, and not limitation, handheld devices (e.g., smartphones) and/or vehicles (e.g., including V2X communication) require mobility. As such, when the handheld devices and/or the vehicles move, a location of the edge application may no longer be optimal, which is why the system must be operable to provide UE mobility. In one embodiment, the application instance is changed from a first location to a second location. In one embodiment, user context is transferred from the first location to the second location for a stateful application.

In one embodiment, an application is a stateful service. In one embodiment, application mobility for the stateful services includes transferring and synchronizing a service state between a first application instance and a second application instance. Advantageously, this process provides service continuity. In one embodiment, the application is constructed and configured to allow multiple instances of the application to run concurrently. In one embodiment, the service state of the application is operable to be captured in the first application instance and transferred to the second application instance independent of operation of the instance itself. In one embodiment, the second application instance is operable to continue in a second MEC host without disruption of service when the UE disconnects from the first application instance in a first MEC host. Alternatively, the application is a stateless service. In one embodiment, the stateless service does not require transferring and synchronizing the service state between the first application instance and the second application instance. Advantageously, the system is operable to provide application mobility within the MEC system.

As previously described, in a preferred embodiment, the system is operable to provide application mobility. In one embodiment, service to a UE resumes when a user's context and/or application instance is relocated from a first MEC host to a second MEC host. FIG. 59 illustrates one embodiment of application mobility in an integrated MEC deployment.

In one embodiment, application mobility features include a plurality of procedures. In one embodiment, the plurality of procedures includes, but is not limited to, application mobility enablement, detection of UE movement, validation of application mobility, user context transfer, application instance relocation, and/or post-post processing of application relocation. In one embodiment, implementation of the plurality of procedures is dependent on characteristics of the application, characteristics of the environment, and/or capabilities of the system (e.g., MEC host, MEC orchestrator, MEC application).

In one embodiment, application mobility is triggered by detection of UE movement from a first serving cell to a second serving cell. In one embodiment, application mobility involves the NEF and/or MEC functional entities. In one embodiment, the MEC functional entities are operable to subscribe to relevant event notification. In one embodiment, the MEC platform subscribes to radio network information produces by the Radio Network Information Service (RNIS). In one embodiment, the radio network information is operable to identify at least one UE moving from the first serving cell to the second serving cell. In one embodiment, the radio network information is operable to determine whether the at least one UE is moving from a first service area of a first MEC host to a second service area of a second MEC host.

In one embodiment, the NEF is operable to expose capability information and/or services operable to be provided by core network functions to at least one external entity. In one embodiment, the at least one external entity includes at least one application function (AF) (e.g., MEC system functional entities). In one embodiment, SBA enables an authorized AF to directly access a network function. In one embodiment, services are exposed over NEF. In one embodiment, the services exposed over NEF include, but are not limited to, monitoring, provisioning, and/or policy and charging.

In one embodiment, monitoring provides for an external entity to request and/or subscribe to UE related events of interest. In one embodiment, the UE related events of interest include, but are not limited to, a roaming status of a UE, loss of connectivity of the UE, reachability of the UE, and/or location related events (e.g., location of a specific UE, identification of a UE in a geographical area). In one embodiment, the AMF and/or the UDM are operable to provide information about the UE related events of interest (e.g., predicted UE movement, communication characteristics).

In one embodiment, provisioning provides for an external entity to provision expected UE behavior in the system (e.g., predicted UE movement, communication characteristics).

In one embodiment, policy and charging is operable to handle quality of service (QoS) and charging policy for UE based requests from at least one external party. In one embodiment, policy and charging facilitates sponsored data services. In one embodiment, Policy and Charging Control (PCC) is governed by the Policy Control Function (PCF). In one embodiment, the PCC is supported by at least one NF.

FIG. 59 illustrates an example of 5G capability exposure to the MEC system. In the embodiment shown in FIG. 59 , the MEC orchestrator is an AF and is operable to provide centralized functions for managing computing resources and operation of MEC hosts. The MEC orchestrator is operable to provide orchestration of MEC application running on the MEC hosts. In one embodiment, the MEC orchestrator is operable to interact with the NEF and/or network functions to provide monitoring, provisioning, and/or policy and charging. In one embodiment, the MEC host is deployed at the edge of the RAN. In one embodiment, the MEC platform is directly exposed to the CUs and/or the DUs of the RAN. Advantageously, this provides real-time radio information related to UEs, limiting latency and bandwidth consumption.

In one embodiment, the MEC system is operable to provide add-on services to at least one MEC application by leveraging network capability information from the RAN as shown in FIG. 60 . In one embodiment, the MEC platform and/or a MEC application is operable to determine a location of a UE using real-time signal information. In one embodiment, the MEC platform and/or the MEC producing application is operable to use the location to expose the UE to a MEC service consuming application via a MEC location service. In one embodiment, the location is operable to be transmitted to the 5G network. In one embodiment, the location is operable to be provided to the NEF to be used in predicting UE location. In one embodiment, the location is operable to be used to provide optimization of service and/or location-based services (LBSs) (e.g., location-based marketing) to the UE.

In one embodiment, the Mx2 reference point is positioned between the device application and the User Application Lifecycle Management proxy as shown in FIG. 61 . In one embodiment, the system includes a UE Application API over the Mx2 reference point. In one embodiment, the UE Application API is operable to allow the device application to request at least one lifecycle management action in the MEC system (e.g., requesting a list of available MEC applications, instantiation of at least one MEC application, termination of at least one MEC application). In one embodiment, the UE Application API is operable to allow the device application to receive at least one notification of a change in the MEC application's IP address. The MEC applications instantiated in a MEC host in response to a request via a device application are referred to as user applications.

In one embodiment, the UE Application API is operable to assist the MEC system with application and/or context relocation. In one embodiment, the UE Application API is operable to assist the MEC system with application relocation between a first MEC system and a second MEC system. In one embodiment, the UE Application API is operable to assist the MEC system with application relocation between the MEC system and a cloud system.

In one embodiment, MEC services are provided by at least one Mobile Network Operator (MNO). Additionally or alternatively, the MEC services are provided by at least one third party. In one embodiment, the at least one third party is a cloud service provider, a venue owner, a facility owner, a management company, a cell tower provider, a neutral host vendor, and/or a fleet management company. In one embodiment, the at least one MNO leases or buys edge cloud services from the at least one third party.

FIG. 62 illustrates one embodiment of a third-party cloud for MEC in a 5G network environment. The Network Exposure Function (NEF) is operable to be used an entry point into the 5G network for at least one authorized third party. The at least one authorized third party is operable to configure application traffic in the user plane and direct the application traffic to at least one MEC application in a local data network (LDN). The NEF is also operable to provide network information (e.g., mobility information, radio resource information) to the MEC system. The NEF is operable to handle control functions to manage MEC operations (e.g., for the at least one authorized third party). Advantageously, this provides separation between the MNO and the at least one authorized third party.

Traffic on the user place is directed to MEC applications via configuration and placement of UPF functions. The configuration and placement of the UPF functions is operable to be influenced (e.g., by third party cloud service providers) via a control interface exposed through the NEF.

MEC enables serverless computing by hosting Function as a service (FaaS) at the edge and integrating with a cloud service provider. FaaS is operable to be implemented via at least one cloud wrapper MEC application running on at least one MEC host. A MEC service application is operable to manage local resources.

FIG. 63 illustrates one embodiment of a MEC host with and without a break-out to cloud.

Traffic is operable to be sent back to an IoT device, sent to a cloud service provider from a cloud wrapper MEC application, or transferred to an alternate MEC application with sufficient resources. The MEC application and/or the MEC service is operable to indicate to the AF to initiate traffic steering to an alternative MEC host with sufficient resources. In one embodiment, the indication includes traffic rule activation over an application enablement API.

FIG. 64 illustrates one embodiment of traffic steering to an alternative MEC host.

MEC is operable to support enterprise applications and provide enterprise connectivity. For example, and not limitation, MEC-based applications are expected to benefit a plurality of industry sectors (e.g., traffic management, healthcare, government entities). Network slicing and flexibility of UPF deployment are operable to enable the enterprise applications for the plurality of industry sectors.

In one embodiment, a network includes ultra-reliable low latency communications (URLLC) including local processing (e.g., in an edge cloud). Advantageously, the URLLC provides benefits for IoT devices. In one embodiment, the edge cloud is used in Massive IoT (e.g., hundreds to billions of connected devices or sensors). Network slicing provides dedicated resources for tenants (e.g., for IoT). Additional information is included in ETSI, MEC in 5G networks, ETSI White Paper No. 28, ISBN No. 979-10-92620-22-1 (June 2018), which is incorporated herein by reference in its entirety.

In one embodiment, the MEC provides traffic steering and policy control information of applications. In one embodiment, information is exchanged between the MEC and the network exposure function (NEF). See, e.g., ETSI: ETSI GS MEC 002 Multi-access Edge Computing (MEC); Phase 2: Use Cases and Requirements, v2.1.1. ETSI MEC ISG (October 2018), which is incorporated herein by reference in its entirety.

In one embodiment, the system includes an NFV MANO as part of the management and orchestration domain. In one embodiment, the NFVO and/or the VFNM are operable to provide life cycle management of the MEC platform and/or the MEC applications. In one embodiment, the NFV infrastructure (NFVI) is operable to deploy the MEC applications, the MEC platforms, and/or the MEC platform managers.

In one embodiment, a slice is included in the MEC. In one embodiment, the MEC Platform (MEP) and the MEC Platform Management (MEPM) entities are operable to be shared by more than one slice. In one embodiment, each slice includes a MEP and a MEPM. In one embodiment, the MEC includes a MEC Applications Orchestrator (MEAO) and/or a virtualized MEPM (MEPM-V). In one embodiment, each slice is isolated from other slices.

In one embodiment, the MEC includes procedures for migration and/or service continuity of applications. In one embodiment, the procedures include, but are not limited to, MEC host pre-allocation based on UE movement prediction and/or creation of relocation groups. In one embodiment, the relocation groups include at least one MEC host pre-configured to run at least one application. Advantageously, pre-configuring the at least one MEC host to run the at least one application reduces deployment time required for a handover. See, e.g., ETSI: ETSI GR MEC 018, Multi-access Edge Computing (MEC); End to End Mobility Aspects, v1.1.1 ETSI MEC ISG (October 2017), which is incorporated herein by reference in its entirety.

In one embodiment, the MEC is defined as a separate orchestration domain. In one embodiment, the MEC is implemented on the same NFVI and VNFs. In one embodiment, the MEC is hosted by the ETSI NFV MANO stack of the VNF domain. The MEC is operable to reduce latency, offload computation, scale data, and/or offload some network functions.

In one embodiment, the system is operable to perform network slicing. In one embodiment, the network slicing is based on an ETSI NFV MANO architecture. In one embodiment, the network slicing is operable to support tenant-oriented operations and interfaces. In one embodiment, the network slicing includes at least one embedded in-slice manager.

In one embodiment, the system includes a Distributed Autonomous Slice Management and Orchestration (DASMO), which is illustrated in FIG. 65 . DASMO is described in S. Kukliński and L. Tomaszewski, “DASMO: A scalable approach to network slices management and orchestration,” NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium, 2018, pp. 1-6, doi: 10.1109/NOMS.2018.8406279, which is incorporated herein by reference in its entirety. In one embodiment, a slice includes a core. The core includes a plurality of functions and/or at least one functional block. In one embodiment, the plurality of functions includes, but is not limited to, application virtual network functions (A-VNFs), control virtual network functions (C-VNFs), management virtual network functions (M-VNFs), and/or support virtual network functions (S-VNFs). In one embodiment, the at least one function includes a Slice Manager (SM) and/or a Slice Operation Support (SOS). In one embodiment, the SM is operable to implement M-VNFs. In one embodiment, the SOS is operable to implement S-VNFs.

The SM includes connections to Embedded Element Managers (EEMs) of VNFs implemented within a slice. The EEMs are operable to provide slice-level management support, VNF monitoring, actuating, and/or autonomic control loops. In one embodiment, the SM is operable to provide a real-time feedback loop. In one embodiment, the real-time feedback loop is compatible with a Monitor-Analyze-Plan-Execute (MAPE) model. In one embodiment, the SM further provides accounting, KPI monitoring and reporting, and/or configuration support as tenant-oriented functions. In one embodiment, the SM includes an interface to the global OSS/BSS. In one embodiment, SOS functions are operable to provide slice-level operations including, but not limited to, slice selection, subscription, authentication, and/or stitching of sub-slices.

In one embodiment, the SM is centrally located within the slice management plane. In one embodiment, the SM is linked to Embedded Element Managers (EEMs) of VNFs implemented within a slice. In one embodiment, the EEMs are compatible with ETSI NFV concepts of an Element Manager (EM). In one embodiment, the EEMs are operable to include

Advantageously, the in-slice management (ISM) concept is scalable. For example, and not limitation, orchestration is operable to be scaled using recursive orchestration (e.g., “MANO in MANO”). Further, this ISM concept is compatible with DASMO. In one embodiment, slicing architecture of the MEC is based on a plurality of factors including, but not limited to, limited geographic scope, specificity of services, flexible architecture, implementation of MEC applications as part of slice AP, and/or tight integration of MEC APIs.

FIG. 66 illustrates one embodiment of proposed generalized MEC architecture. In one embodiment, the VNFs are implemented in a VNF space. In one embodiment, the VNFs utilize a common NFVI managed by a VIM. In one embodiment, the NFVI is a single-domain. Alternatively, the NFVI is a multi-domain. In one embodiment, the VNFs have EMs (shown in gray dots) connected to the OSS/BSS (shown using gray arrows). The MEC Apps include management functions. In one embodiment, the management functions are embedded in the applications. The VNFs and the Ems are connected to at least one VNFM. The at least one VNFM is operable to provide life cycle management of MEC applications and VNFs. In one embodiment, all interactions with the NFV MANO stack are provided by an OSS-NFVO interface. In one embodiment, at least one MEC reference point is internalized. In one embodiment, the OSS/BSS opens the Mx1 interface and/or the Mx2 interface to the customer domain.

In one embodiment, the system includes a MEP-MEP-V (Variant 1). In FIG. 66 , the “VNF space” is operable to be renamed as “5G Network” using Variant 1. Alternatively, the system includes a MEP/MEPM-V shared by multiple networks (Variant 2). In Variant 2, the MEP-MEPM-V are dedicated and external to 5G networks. In one embodiment, the system is operable to provide network privacy via externalized MEP relative to connected networks (e.g., in Variant 2). In one embodiment, the system is operable to provide inter-application privacy.

Moving network functions to the edge of a distributed network decreases latency and increases performance but reduces control and management of the system overall. Distributed networks generally do not use centralized management due to the inefficiencies created by transporting data to a centralized location.

FIG. 67 illustrates one embodiment of DASMO extended with MEC. Each VNF has a corresponding EEM, which is required in DASMO. Each corresponding EEM is connected to the SM. The MEAO and the User App LCM proxy are located in the SM. The SM is operable to provide routing of data. The MEP and the MEPM-V are located in the SOS.

The DASMO architecture is also operable to support multi-domain sliced networks. In one embodiment, the global OSS/BSS includes Multi-Domain Management and Orchestration Support functions including a Multi-Domain Slice Configurator (MDSC) and/or a Multi-Domain Orchestrator (“Umbrella NFVO”). In one embodiment, the MDSC is operable to monitor the end-to-end slice and/or coordinate reconfiguration of the end-to-end slice. In one embodiment, the MDSC is operable to configure local SOS entities for inter-domain operations.

In one embodiment, the system is operable to provide horizontal end-to-end slice stitching to enable operations in a multi-domain environment. In one embodiment, the system utilizes Inter-Domain Operations Support (DOS) to provide the horizontal end-to-end slice stitching. In one embodiment, the DOS is operable to provide exchange of information between neighboring domains. In one embodiment, the Mp3 reference point is operable to control information transfer between MEPs. In one embodiment, the information transfer is via DOS.

In one embodiment, the system includes service APIs exposed to MEC applications by the MEP. In one embodiment, the service APIs include, but are not limited to, Radio Network Information, Location, UE Identity, and/or Bandwidth Management. In one embodiment, the service APIs are provided via the Mp2 reference point.

In one embodiment, the MEC is where the RF environmental information and the customer goals are used to optimize the customer utilization of spectrum and RF environment. The RF environmental information collected and analyzed in the RU, DU, and/or CU are first combined with customer goals to filter and use only relevant data (e.g., actionable data) to optimize customer utilization of the RF environment and spectrum.

In the RU, DU, and/or CU, the environment is collected, aggregated, and analyzed to obtain RF environmental awareness information. This is represented in a vector ensemble class (SD_(t)) for each signal in the RF environment. If N signals are detected in the RF environment, then this ensemble is operable to be represented by the following group:

${SD}_{t} = {\overset{N}{\bigcup\limits_{i = 1}}S_{c_{i}}}$ where S_(c) _(i) is the ith detected signal information. The detected signal information includes, but is not limited to, a center frequency (f_(c) _(i) ), a bandwidth (BW_(i)), a power (P_(i)), a signal to noise ratio (SNR_(i)), a signal to total noise and interference ratio (SNIR_(i)), a modulation type (M_(i)), a type of signal (T_(i)), a location of signal or angle of arrival relative to the RU (L_(i)), an antenna index (A_(i)), an arrival rate (ar_(i)), a time of arrival of signal i to antenna index j (TOA_(i,j)), a priority latency (PL_(i)), an interaction vector (I_(v) _(i) ) (e.g., list of signals that the ith signal interacts with), customer jth actionable data or information (ADI_(j)), a lower frequency component of signal i (f_(L) _(i) ), an upper frequency component of signal i (f_(U) _(i) ), and/or a time duration of the ith signal (TD_(i)). S_(c) _(i) is operable to be represented by the following, among other environmental RF awareness metadata: S _(c) _(i) =[f _(c) _(i) ,BW_(i) ,P _(i),SNR_(i),SNIR_(i) ,M _(i) ,T _(i) ,L _(i) ,A _(i),ar_(i),TOA_(i,j),PL_(i) ,I _(v) _(i) ,ADI_(j) ,f _(L) _(i) ,TD_(i), . . . ]

The signal information is then analyzed for all signals in the capture environment. Statistical information (e.g., simple statistics) of the signals are obtained, and possible interactions are analyzed {I_(v) _(i) }. The customer goals are analyzed and actionable data is extracted on SD_(t) for the customer. This operation is operable to be performed for a plurality of customers simultaneously (e.g., using the vectors described herein).

As seen in FIG. 68 , the CU passes the set SD_(t) containing all information available for each signal in the environment to the MEC application. The following steps are performed. First, each signal in the SD_(t) is analyzed for possible interactions. For example, and not limitation:

a) Center frequency: f_(c) _(i) =f_(c) _(i) ±Δ_(i)

b) For the jth signal: f_(L) _(i) ≤f_(L) _(j) <f_(L) _(i) +BW_(i)

c) For the jth signal: f_(L) _(i) <f_(L) _(j) +BW_(j)<f_(U) _(i)

If these conditions are satisfied, then i and j interact and power levels are checked for lower estimate of SNIR_(i) contribution of that interaction for the signal i and s_(j) is added to IV_(i).

Once all possible interaction between signals in the SD_(t) are detected, a new set is created by adding the set {IV_(i)}_(i) ^(v) to SD_(t). The resulting set SD_(t) is then combined with the customer goals index vector {CG_(i=1)}_(i) ^(M), which is a vector of binary values {0,1}, where 1 in the Kth index denotes the SC_(i). K index ∀_(i) is used for further analysis to satisfy the customer goals: CG_(j)=[a ₁ a ₂ . . . a _(IL)] where a_(j)∈{0,1} and IL is the length of SC_(i) ∀_(i). This result is the ADI_(i), which is the actionable data information for customer i.

The customer goal index vector for the jth customer is obtained by a semantic engine. The semantic engine is operable to associate information required for each goal requested and assign a one or a zero to each index in SC_(i). In a preferred embodiment, a one is assigned if the information is relevant and a zero is assigned if the information is not relevant. Alternatively, a zero is assigned if the information is relevant and a one is assigned if the information is not relevant. This is performed in the relevant statistical logic function. Additionally, this function uses a constraint vector

h_(c_(p))]_(p = 1)^(M) is generated if and only if information about the network resources (e.g., both transmitter and receiver of each node on the network) are known and part of the customer goals information supplied to the function (e.g., during initialization or setup). This constraint vector is used to set order of importance for the usage of each resource. Each customer goal generates a unique set of constraint vectors. An additional factor influencing this constraint vector includes, but is not limited to, the granularity at which the network resources are operable to be accessed. In general, the optimization is operable to be expressed as follows: J _(ru)=min_(ru)[G(ADI_(t))+λ_(C) ^(T) A(ru)−CGV] where ru is the resource unit, G(⋅) is a function defined by customer goals, A(⋅) is a function defined by the customer goals and the available resource units in the network, and CGV is the optimal values reflecting the customer goals under ideal circumstances. G(⋅), A(⋅), and CGV are dictated by the customer goals and network resource knowledge, which are a priori information (e.g., loaded during initialization or set up).

Before proceeding with examples of customer goals, the network resources available for optimization must first be established. The following examples begin with simple examples and move to more comprehensive examples relevant to current 4G, 5G, and new 6G networks.

Example 1

The first example includes a customer goal to minimize interference for a transmitted signal (s₂).

FIG. 69 illustrates one example of an RF environment. In the example shown, a first signal (s₁) is a QPSK signal having a power P₁ with f_(1L)=3 MHz, f_(1U)=6 MHz, BW₁=3 MHz, L₁=0°, TOA₁=15 samples, SNR₁=20, SNIR₁=9.2 dB, M₁=3 (QPSK), A₁=1, ar₁=1, P_(L) ₁ =0 dB, I_(v) ₁ =[2], and TD₁=1. A second signal (s₂) is the customer signal, which is a QPSK signal having a power P₂ similar to P₁ with f_(2L)=4 MHz, f_(2U)=11 MHz, BW₂=7 MHz, L₂=0°, TOA₂=40 samples, SNR₂=20, SNIR₂=2.92 dB, M₂=3 (QPSK), A₂=1, ar₂=1, P_(L) ₂ =0 dB, I_(v) ₁ =[1,3], and TD₂=1. A third signal (s₃) is a 16-QAM signal having a power P₃ that is 3 dB above P₁ and P₂ with f_(3L)=9 MHz, f_(3U)=13 MHz, BW₃=4 MHz, L₃=0°, TOA₃=20 samples, SNR₃≈23, SNIR₃≈18 dB, M₃=16 (16-QAM), A₃=1, ar₃=1, P_(L) ₃ =0 dB, I_(v) ₃ =[2], and TD₃=1. A fourth signal (s₄) is a BPSK signal having a power P₄ that is 1 dB above P₁ and P₂ with f_(4L)=4 MHz, f_(4U)=12 MHz, BW₄=8 MHz, L₄=30°, TOA₄=100 samples, SNR₄≈21 dB, SNIR₄≈37 dB, M₄=1 (BPSK), A₄=1, ar₄=1, P_(L) ₄ =0 dB, I_(v) ₄ =[Ø], and TD₄=1.

The customer goal is to minimize interference, then actionable data relevant to customer 2 is included in the following vector: ADI₂=[s _(c) ₂ ,M ₁ ,M ₃ ,P ₁ ,P ₃ ,f _(1U) ,f _(1L) ,f _(3U) ,f _(3L),BW₁,BW₃ ,L ₁ ,L ₃]

Optimization according to the goal of reducing overall SINR for signal 2 and reducing interference while only controlling the parameters associated with signal s₂ provides the following equation: G=SINR for s ₂ based on ADI₂

First, if BW₂ is operable to be adjusted (e.g., no throughput constraint), then G is operable to be calculated as follows:

${G\left( {ADI}_{2} \right)} = {10\mspace{11mu}{\log\left\lbrack \frac{P_{2}}{N_{2} + {\left( {f_{1\; U} - f_{2\; L}} \right)\frac{P_{1}}{{BW}_{1}}} + {\left( {f_{2\; U} - f_{3L}} \right)\frac{P_{3}}{{BW}_{3}}}} \right\rbrack}}$

The A(⋅) function is calculated as: A ₂(⋅)=SNR₂+SINR₂ and CVG₂ is calculated as follows: CGV₂=SNR₂

The optimization is given as follows: J _(ru)=min_(RU) ₂ [G(ADI₂)+λ(SNR₂+SINR₂−SNR₂)] where min_(RU) ₂ is minimized over all parameters (e.g., P₂, f_(2L), f_(2U), BW₂, etc.). This results in the following equation (eq. A):

$J_{ru} = {\min_{{RU}_{2}}\left\lbrack {{10\mspace{11mu}{\log\left\lbrack \frac{P_{2}}{N_{2} + {\left( {f_{1\; U} - f_{2\; L}} \right)\frac{P_{1}}{{BW}_{1}}} + {\left( {f_{2\; U} - f_{3\; L}} \right)\frac{P_{3}}{{BW}_{3}}}} \right\rbrack}} + {\lambda\left( {{SNR}_{2} + {SINR}_{2} - {SNR}_{2}} \right)}} \right\rbrack}$

If there is no constraint on BW₂ (and throughput, assuming M₂ remains constant), then f_(2L)=f_(1U) and f_(2U)=f_(3L), SINR₂ becomes SNR₂. Thus, the above cost function is minimized to zero and interference is removed from s₂.

However, if BW₂ has a constraint of BW₂=7 MHz, then the above expression is minimized relative to P₂ by making P₂ as large as possible. Typically, there are constraints on P_(max) for each signal. For example, and not limitation, if P_(2max)=10 dBm, P₁=5 dBm, and P₃=8 dBm, the cost function becomes:

${Cost} = {{\left\lbrack {1 + \lambda} \right\rbrack\left\lbrack {10\mspace{14mu}{\log_{10}\left( \frac{10^{({10/10})}}{\frac{10^{({10/10})}}{100} + {10\left( \frac{5(2)}{30} \right)} + {10\left( \frac{8(2)}{40} \right)}} \right)}} \right\rbrack} = {5.98\mspace{14mu}{dB}}}$

Example 2

The second example includes optimizing over the customer transmitting signal 4 (s₄) as well as customer 2 transmitting s₂. The goal of customer 4 is to maximize throughput given a power constraint of R_(4max)=8 dBm.

In this case, the cost function has the previous cost function as its first component and the second component is provided in the following equation: max_(RU) ₄ (f _(4U) −f _(4L))+λ[BW₄−BW_(4max)] where BW_(4max)=15 MHz

Thus, the component cost function becomes: C _(total)=eqA+max_(RU) ₄ (f _(4U) −f _(4L))+λ[BW₄−15] s₄ is at AOA of 30° compared to s₁, s₂, and s₃, which are at 0°. Thus, the spatial filtering separates both signals. Therefore, the two components of the compound cost function are operable to be treated as two optimizations because I_(v) ₄ =[Ø] and I_(v) ₂ =[1,3], so it does not contain s₄. Therefore, in this example, the second optimization results in f_(4U)=15 MHz and f_(4L)=0 MHz for BW₄=BW_(max)=15 MHz.

Example 3

The third example includes utilization of spectrum in the Citizens Broadband Radio Service (CBRS) band (3.4 to 3.55 GHz) for private wireless networks. A private wireless network provides a private cellular network for use by a customer. In one embodiment, the private wireless network uses cellular infrastructure both for the customer access point and user equipment (e.g., based on 4G LTE or 5G protocols). In one embodiment, the private wireless network uses spectrum in the CBRS band in the unlicensed portion of the 150 MHz (the General Authorized Access (GAA) portion). In one embodiment, the private wireless network uses Spectrum Access System (SaS) allocation methodology. In one embodiment, the allocation is based on information stored in at least one database. In one embodiment, the information includes information about at least one network access point (e.g., all network access points) for the private wireless network. In one embodiment, the information includes, but is not limited to, transmitter power, static location, bandwidth requirements, protocol used, priority of service, and/or other quality of service requirements supported. In one embodiment, the system includes propagation models to estimate spectrum utilization by at least one user in at least one user location by at least one CBRS registered device (e.g., all CBRS registered devices).

In one embodiment, the allocation of spectrum is based on propagation models estimation of spectrum utilization. However, as a number of CBRS registered devices increases and a number of private wireless networks grows, the propagation modeling will become more computationally complex and less flexible (e.g., exponential computational growth will make this prohibitive). As the number of services used in the private network and the number of private networks increases, interference will increase between services trying to use the same and/or adjacent frequencies. Thus, there is a need to track interference events affecting a mission critical service provided by a private wireless network.

The present invention is operable to detect and/or track at least one interference events. Information about the at least one interference event is operable to be provided to the SaS to allow for a more effect reallocation of the spectrum among CBRS network users. In one embodiment, one or more of the at least one interference event is parameterized based on at least one intended service requirement. In one embodiment, the present invention uses RF environmental information (e.g., channel spectrum utilization by at least one other signal), not signals associated with the intended service or signals associated with the signal itself (e.g., S_(c) _(i) ).

In one embodiment, interference between an intended service's signal and at least one signal affecting the intended service's signal is defined to select a channel in the CBRS band that is optimal for the service intended. In one embodiment, in a similar case to the first example discussed above, the channel is selected to minimize the interference to the intended service by using RF environmental information from the interference signals to predict their spectrum utilization and recommend to the network operator the channel with less interference to the intended service.

In one embodiment, interference event information and/or recommended spectrum preference for the private wireless network are provided to the SaS. In one embodiment, the interference event is reported to the SaS.

FIG. 71A illustrates one embodiment of an interference template including interference metrics, adjacent channel measurements, and geolocation.

FIG. 71B illustrates one embodiment of an interference template including additional evidence of external interference. The interference template preferably includes the interference metrics, the adjacent channel measurements, the geolocation, and the additional evidence of external interference.

FIGS. 72-73 represent an example of the quality of the channels, after processing by the analytical engine, associated with the CBRS spectrum. This allows the private wireless network operator the ability to request the best channel for their mission critical applications. The figures represent a GUI for interference detection and minimization as well as a spectrum utilization optimization display for the private wireless operator in the CBRS band.

FIG. 72 illustrates one example of a graphical user interface (GUI) for a channel average power. In the example shown in FIG. 72 , channelized trend analysis is shown over a 10 second interval. Other time intervals are compatible with the present invention (e.g., using the time aggregation feature). In one embodiment, the GUI is operable to display a plurality of channel center frequencies and select one or more frequencies from the plurality of channel center frequencies. In one embodiment, the one or more frequencies are displayed using a plurality of colors (e.g., a first frequency is displayed using a first color, a second frequency is displayed using a second color, etc.).

FIG. 73 illustrates one example of a GUI for channel availability. In one embodiment, the GUI provides selection between a plurality of RF scenarios (e.g., a first environment, a second environment, a third environment, etc.). In one embodiment, the GUI provides selection of channel status (e.g., all, unoccupied channels, occupied channels). In one embodiment, the channel status is displayed using a plurality of colors (e.g., a first channel status is displayed using a first color, a second channel status is displayed using a second color). In the example shown in FIG. 73 , the occupied channels are displayed in red and the unoccupied channels are displayed in green. In one embodiment, the GUI provides selection of a requested bandwidth and provides an option to reset the requested bandwidth. In one embodiment, the selection is between a set of fixed values. Alternatively, the selection is adjustable (e.g., via manual input). In one embodiment, the GUI provides a selection of requested reliability (e.g., low, medium, high) and provides an option to reset the requested reliability. In one embodiment, a requested Effective Radiated Power (ERP) is selectable (e.g., via manual input).

The system of the present invention is operable to utilize a plurality of learning techniques including, but not limited to, machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), and/or natural language processing (NLP). The system is operable to use any of the aforementioned learning techniques alone or in combination.

Further, the system is operable to utilize predictive analytics techniques including, but not limited to, machine learning (ML), artificial intelligence (AI), neural networks (NNs) (e.g., long short term memory (LSTM) neural networks), deep learning, historical data, and/or data mining to make future predictions and/or models. The system is preferably operable to recommend and/or perform actions based on historical data, external data sources, ML, AI, NNs, and/or other learning techniques. The system is operable to utilize predictive modeling and/or optimization algorithms including, but not limited to, heuristic algorithms, particle swarm optimization, genetic algorithms, technical analysis descriptors, combinatorial algorithms, quantum optimization algorithms, iterative methods, deep learning techniques, and/or feature selection techniques.

Additionally, the system is operable to employ control theory concepts and methods. This enables the system to determine if every data set processed and/or analyzed by the system represents a sufficient statistical data set.

Location data is created in the present invention using one or more hardware and/or software components. By way of example and not limitation, location data is created using the Global Positioning System (GPS), low energy BLUETOOTH based systems such as beacons, wireless networks such as WIFI, Radio Frequency (RF) including RF Identification (RFID), Near Field Communication (NFC), magnetic positioning, and/or cellular triangulation. By way of example, location data is determined via an Internet Protocol (IP) address of a device connected to a wireless network. A wireless router is also operable to determine identities of devices connected to the wireless network through the router, and thus is operable to determine the locations of these devices through their presence in the connection range of the wireless router.

FIG. 70 is a schematic diagram of an embodiment of the invention illustrating a computer system, generally described as 800, having a network 810, a plurality of computing devices 820, 830, 840, a server 850, and a database 870.

The server 850 is constructed, configured, and coupled to enable communication over a network 810 with a plurality of computing devices 820, 830, 840. The server 850 includes a processing unit 851 with an operating system 852. The operating system 852 enables the server 850 to communicate through network 810 with the remote, distributed user devices. Database 870 is operable to house an operating system 872, memory 874, and programs 876.

In one embodiment of the invention, the system 800 includes a network 810 for distributed communication via a wireless communication antenna 812 and processing by at least one mobile communication computing device 830. Alternatively, wireless and wired communication and connectivity between devices and components described herein include wireless network communication such as WI-FI, WORLDWIDE INTEROPERABILITY FOR MICROWAVE ACCESS (WIMAX), Radio Frequency (RF) communication including RF identification (RFID), NEAR FIELD COMMUNICATION (NFC), BLUETOOTH including BLUETOOTH LOW ENERGY (BLE), ZIGBEE, Infrared (IR) communication, cellular communication, satellite communication, Universal Serial Bus (USB), Ethernet communications, communication via fiber-optic cables, coaxial cables, twisted pair cables, and/or any other type of wireless or wired communication. In another embodiment of the invention, the system 800 is a virtualized computing system capable of executing any or all aspects of software and/or application components presented herein on the computing devices 820, 830, 840. In certain aspects, the computer system 800 is operable to be implemented using hardware or a combination of software and hardware, either in a dedicated computing device, or integrated into another entity, or distributed across multiple entities or computing devices.

By way of example, and not limitation, the computing devices 820, 830, 840 are intended to represent various forms of electronic devices including at least a processor and a memory, such as a server, blade server, mainframe, mobile phone, personal digital assistant (PDA), smartphone, desktop computer, netbook computer, tablet computer, workstation, laptop, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the invention described and/or claimed in the present application.

In one embodiment, the computing device 820 includes components such as a processor 860, a system memory 862 having a random access memory (RAM) 864 and a read-only memory (ROM) 866, and a system bus 868 that couples the memory 862 to the processor 860. In another embodiment, the computing device 830 is operable to additionally include components such as a storage device 890 for storing the operating system 892 and one or more application programs 894, a network interface unit 896, and/or an input/output controller 898. Each of the components is operable to be coupled to each other through at least one bus 868. The input/output controller 898 is operable to receive and process input from, or provide output to, a number of other devices 899, including, but not limited to, alphanumeric input devices, mice, electronic styluses, display units, touch screens, gaming controllers, joy sticks, touch pads, signal generation devices (e.g., speakers), augmented reality/virtual reality (AR/VR) devices (e.g., AR/VR headsets), or printers.

By way of example, and not limitation, the processor 860 is operable to be a general-purpose microprocessor (e.g., a central processing unit (CPU)), a graphics processing unit (GPU), a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated or transistor logic, discrete hardware components, or any other suitable entity or combinations thereof that can perform calculations, process instructions for execution, and/or other manipulations of information.

In another implementation, shown as 840 in FIG. 70 , multiple processors 860 and/or multiple buses 868 are operable to be used, as appropriate, along with multiple memories 862 of multiple types (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core).

Also, multiple computing devices are operable to be connected, with each device providing portions of the necessary operations (e.g., a server bank, a group of blade servers, or a multi-processor system). Alternatively, some steps or methods are operable to be performed by circuitry that is specific to a given function.

According to various embodiments, the computer system 800 is operable to operate in a networked environment using logical connections to local and/or remote computing devices 820, 830, 840 through a network 810. A computing device 830 is operable to connect to a network 810 through a network interface unit 896 connected to a bus 868. Computing devices are operable to communicate communication media through wired networks, direct-wired connections or wirelessly, such as acoustic, RF, or infrared, through an antenna 897 in communication with the network antenna 812 and the network interface unit 896, which are operable to include digital signal processing circuitry when necessary. The network interface unit 896 is operable to provide for communications under various modes or protocols.

In one or more exemplary aspects, the instructions are operable to be implemented in hardware, software, firmware, or any combinations thereof. A computer readable medium is operable to provide volatile or non-volatile storage for one or more sets of instructions, such as operating systems, data structures, program modules, applications, or other data embodying any one or more of the methodologies or functions described herein. The computer readable medium is operable to include the memory 862, the processor 860, and/or the storage media 890 and is operable be a single medium or multiple media (e.g., a centralized or distributed computer system) that store the one or more sets of instructions 900. Non-transitory computer readable media includes all computer readable media, with the sole exception being a transitory, propagating signal per se. The instructions 900 are further operable to be transmitted or received over the network 810 via the network interface unit 896 as communication media, which is operable to include a modulated data signal such as a carrier wave or other transport mechanism and includes any delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics changed or set in a manner as to encode information in the signal.

Storage devices 890 and memory 862 include, but are not limited to, volatile and non-volatile media such as cache, RAM, ROM, EPROM, EEPROM, FLASH memory, or other solid state memory technology; discs (e.g., digital versatile discs (DVD), HD-DVD, BLU-RAY, compact disc (CD), or CD-ROM) or other optical storage; magnetic cassettes, magnetic tape, magnetic disk storage, floppy disks, or other magnetic storage devices; or any other medium that can be used to store the computer readable instructions and which can be accessed by the computer system 800.

In one embodiment, the computer system 800 is within a cloud-based network. In one embodiment, the server 850 is a designated physical server for distributed computing devices 820, 830, and 840. In one embodiment, the server 850 is a cloud-based server platform. In one embodiment, the cloud-based server platform hosts serverless functions for distributed computing devices 820, 830, and 840.

In another embodiment, the computer system 800 is within an edge computing network. The server 850 is an edge server, and the database 870 is an edge database. The edge server 850 and the edge database 870 are part of an edge computing platform. In one embodiment, the edge server 850 and the edge database 870 are designated to distributed computing devices 820, 830, and 840. In one embodiment, the edge server 850 and the edge database 870 are not designated for distributed computing devices 820, 830, and 840. The distributed computing devices 820, 830, and 840 connect to an edge server in the edge computing network based on proximity, availability, latency, bandwidth, and/or other factors.

It is also contemplated that the computer system 800 is operable to not include all of the components shown in FIG. 70 , is operable to include other components that are not explicitly shown in FIG. 70 , or is operable to utilize an architecture completely different than that shown in FIG. 70 . The various illustrative logical blocks, modules, elements, circuits, and algorithms described in connection with the embodiments disclosed herein are operable to be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application (e.g., arranged in a different order or partitioned in a different way), but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. 

The invention claimed is:
 1. A system for dynamic spectrum utilization management in an electromagnetic environment comprising: a network slice or a subnetwork, wherein the network slice or the subnetwork includes a radio access network (RAN), a core network, and a Multi-Access Edge Computing (MEC) layer; at least one monitoring sensor operable to monitor the electromagnetic environment and to create measured data; a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing the measured data and a programmable rules and policy editor; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment; wherein the programmable rules and policy editor includes at least one rule and/or at least one policy; wherein the wireless network resource optimization application is operable to use analyzed data from the at least one data analysis engine to create actionable data for optimizing network resources; wherein the wireless network resource optimization application creates a customer goals index vector or wherein the wireless network resource optimization application utilizes a constraint vector.
 2. The system of claim 1, wherein the network resources are optimized by reconfiguring at least one parameter of the core network and/or the MEC layer associated with the network slice or the subnetwork.
 3. The system of claim 1, wherein the network resources are optimized by changing at least one physical layer parameter of one or more customer devices and/or applications.
 4. The system of claim 1, wherein one or more of the at least one monitoring sensor is included in a base station.
 5. The system of claim 1, wherein one or more of the at least one monitoring sensor is included in at least one spectrum monitoring unit, wherein the at least one spectrum monitoring unit includes at least one processor and memory, at least one receiver, and the one or more of the at least one monitoring sensor.
 6. The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.
 7. The system of claim 1, wherein one or more of the at least one rule and/or the at least one policy is defined by requirements of at least one customer.
 8. The system of claim 1, wherein the wireless network resource optimization application is operable to activate an alarm.
 9. The system of claim 1, wherein the at least one data analysis engine further includes an identification engine, a classification engine, and/or a geolocation engine.
 10. The system of claim 1, wherein the wireless network resource optimization application generates at least one RAN command to change at least one RAN parameter.
 11. The system of claim 1, wherein the wireless network resource optimization application creates a vector ensemble class for the at least one signal of interest.
 12. The system of claim 11, wherein the vector ensemble class includes one or more of: a center frequency, a bandwidth, a power, a signal to noise ratio, a signal to total noise and interference ratio, a modulation type, a type of signal, a location of the at least one signal of interest, an angle of arrival relative to a radio unit, an antenna index, an arrival rate, a time of arrival of the at least one signal of interest, a priority latency, an interaction vector including other signals with which the at least one signal of interest interacts, customer actionable data or information, a lower frequency component of the at least one signal of interest, an upper frequency component of the at least one signal of interest, and a time duration of the at least one signal of interest.
 13. The system of claim 1, wherein the wireless network resource optimization application creates the actionable data in real time or in near-real time.
 14. The system of claim 1, wherein the wireless network resource optimization application is operable to create the actionable data for one or more customers simultaneously using at least one vector.
 15. The system of claim 1, wherein the actionable data is used to generate at least one knowledge-based decision tree.
 16. A system for dynamic spectrum utilization management in a radio frequency (RF) environment comprising: a network slice or a subnetwork, wherein the network slice or the subnetwork includes a radio access network (RAN), a core network, and a Multi-Access Edge Computing (MEC) layer; at least one RF sensor operable to monitor the RF environment and to create RF data, wherein the at least one RF sensor is included in at least one spectrum monitoring unit, wherein the at least one spectrum monitoring unit includes at least one processor and memory, at least one receiver, and the at least one RF sensor; a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing the RF data; wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the RF environment; wherein the wireless network resource optimization application is operable to use analyzed data from the at least one data analysis engine to create actionable data for optimizing network resources; and wherein the wireless network resource optimization application creates a vector ensemble class for the at least one signal of interest or wherein the wireless network resource optimization application utilizes a constraint vector.
 17. The system of claim 16, wherein the at least one spectrum monitoring unit interfaces with the RAN using Non-Access Stratum (NAS) and Access Stratum (AS) functional layer protocols.
 18. The system of claim 16, wherein the at least one spectrum monitoring unit interfaces with the core through an Access and Mobility Management Function (AMF).
 19. The system of claim 16, wherein the network resources are optimized by reconfiguring at least one parameter of the core network and/or the MEC layer associated with the network slice or the subnetwork.
 20. The system of claim 16, wherein the network resources are optimized by changing at least one physical layer parameter of one or more customer devices and/or applications.
 21. A method for dynamic spectrum utilization management in an electromagnetic environment comprising: providing a radio access network (RAN), a core network, and a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork and a wireless network resource optimization application, wherein the wireless network resource optimization application includes at least one data analysis engine for analyzing the measured data and a programmable rules and policy editor, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy; monitoring the electromagnetic environment using at least one monitoring sensor to create measured data; analyzing the measured data using the at least one data analysis engine to create analyzed data, wherein the at least one data analysis engine includes a detection engine and a learning engine; learning the electromagnetic environment using the learning engine; automatically detecting at least one signal of interest using the detection engine; the wireless network resource optimization application utilizing a constraint vector; the wireless network resource optimization application creating actionable data using analyzed data from the at least one data analysis engine; and optimizing network resources using the actionable data.
 22. The method of claim 21, further including optimizing the network resources by reconfiguring at least one parameter of the core network and/or the MEC layer associated with the network slice or the subnetwork.
 23. The method of claim 21, further including optimizing the network resources by changing at least one physical layer parameter of one or more customer devices and/or applications.
 24. The method of claim 21, further including the wireless network resource optimization application creating a vector ensemble class for the at least one signal of interest including detected signal information.
 25. The method of claim 21, wherein the at least one monitoring sensor is included in a base station and/or at least one spectrum monitoring unit, wherein the at least one spectrum monitoring unit includes at least one processor and memory, at least one receiver, and the at least one monitoring sensor. 